{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.3.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import codecs\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'batch_size': 64,\n",
    "    'lr' : 0.001,\n",
    "    'max_sent_len': 20,\n",
    "    'epochs': 500,\n",
    "    'drops' : [0.1]\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_data(data_path):\n",
    "    \"\"\"\n",
    "    意图识别抽取出label\n",
    "    槽位识别与填充作为命名实体识别问题，对每一个字进行实体标注, ate_time', 'B-target', 'I-date_time', 'I-date_time', 'I-operation', 'I-date_time', 'I-date_time']\n",
    "[ ]:\n",
    "￼\n",
    "​B E I O S\n",
    "    \"\"\"\n",
    "    with codecs.open(data_path,\"r\",encoding=\"utf-8\") as fp:\n",
    "        data = json.load(fp)\n",
    "    texts = [example['text'].replace(\" \",\"\") for example in data]\n",
    "    intent_labels = [example['intent'] for example in data]\n",
    "    \n",
    "    slots_ners = []\n",
    "    count = 0\n",
    "    for example in data:\n",
    "        if 'entities' in example.keys():\n",
    "            text = example['text']\n",
    "            ner = ['O'] * len(text)\n",
    "            slots = example['entities']\n",
    "            for key,val in slots.items():\n",
    "                start_idx = text.find(val)\n",
    "                end_idx = start_idx + len(val) -1\n",
    "                if len(val) == 1:\n",
    "                    ner[start_idx] = 'S-' + key\n",
    "                else:\n",
    "                    ner[start_idx] = 'B-' + key\n",
    "                    ner[end_idx] = 'E-'+ key\n",
    "                    for idx in range(start_idx+1, end_idx):\n",
    "                        ner[idx] = 'I-' + key\n",
    "        else:\n",
    "            text = example['text']\n",
    "            ner = ['O'] * len(text)\n",
    "        slots_ners.append(ner)\n",
    "    print('texts len: ', len(texts))\n",
    "    print('intent_lables len: ',len(intent_labels))\n",
    "    print('slots_ners len: ', len(slots_ners))\n",
    "    return texts, intent_labels, slots_ners        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "texts len:  2542\n",
      "intent_lables len:  2542\n",
      "slots_ners len:  2542\n"
     ]
    }
   ],
   "source": [
    "data_path =\"./dataset/data_v2.json\"\n",
    "max_sent_len = params[\"max_sent_len\"]\n",
    "texts, intent_labels, slots_ners = extract_data(data_path)\n",
    "# l = len(texts) // params['batch_size']\n",
    "# texts = texts[:l*params['batch_size']]\n",
    "# intent =  intent[:l*params['batch_size']]\n",
    "# slots_ners = slots_ners[:l*params['batch_size']]\n",
    "train_text = [d for i , d in enumerate(texts) if i % 10 != 0]\n",
    "train_l = len(train_text) // params['batch_size']\n",
    "train_text = train_text[:train_l*params['batch_size']]\n",
    "valid_text = [d for i , d in enumerate(texts) if i % 10 == 0]\n",
    "valid_l = len(valid_text) // params['batch_size']\n",
    "valid_text = valid_text[:valid_l*params['batch_size']]\n",
    "\n",
    "train_intent = [d for i , d in enumerate(intent_labels) if i % 10 != 0]\n",
    "train_intent = train_intent[:train_l*params['batch_size']]\n",
    "valid_intent = [d for i , d in enumerate(intent_labels) if i % 10 == 0]\n",
    "valid_intent = valid_intent[:valid_l*params['batch_size']]\n",
    "\n",
    "train_ner = [d for i , d in enumerate(slots_ners) if i % 10 != 0]\n",
    "train_ner = train_ner[:train_l*params['batch_size']]\n",
    "valid_ner = [d for i , d in enumerate(slots_ners) if i % 10 == 0]\n",
    "valid_ner =valid_ner[:valid_l*params['batch_size']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2240\n",
      "192\n"
     ]
    }
   ],
   "source": [
    "print(len(train_ner))\n",
    "print(len(valid_ner))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构建文本字符索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_set = []\n",
    "for i in texts:\n",
    "    for j in i:\n",
    "        text_set.append(j)\n",
    "\n",
    "character = ['PADL'] \n",
    "for i in set(text_set):\n",
    "    character.append(i)\n",
    "\n",
    "char2id = {}\n",
    "for index, val in enumerate(character):\n",
    "    char2id.update({val:index})\n",
    "\n",
    "id2char = {}\n",
    "for index, val in enumerate(character):\n",
    "    id2char.update({index:val})    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构建意图索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "intent = ['PADL'] \n",
    "for i in set(intent_labels):\n",
    "    intent.append(i)\n",
    "\n",
    "intent2id = {}\n",
    "for index, val in enumerate(intent):\n",
    "    intent2id.update({val:index})\n",
    "\n",
    "id2intent = {}\n",
    "for index, val in enumerate(intent):\n",
    "    id2intent.update({index:val})  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构建槽位索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "slot_set = []\n",
    "for i in slots_ners:\n",
    "    for j in i:\n",
    "        slot_set.append(j)\n",
    "        \n",
    "slot = ['PADL']\n",
    "for i in set(slot_set):\n",
    "    slot.append(i)\n",
    "    \n",
    "slot2id = {}\n",
    "for index, val in enumerate(slot):\n",
    "    slot2id.update({val:index})\n",
    "\n",
    "id2slot = {}\n",
    "for index, val in enumerate(slot):\n",
    "    id2slot.update({index:val})  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "char = {}\n",
    "char.update({'char2id' : char2id})\n",
    "char.update({'id2char' : id2char})\n",
    "char.update({'intent2id' : intent2id})\n",
    "char.update({'id2intent' : id2intent})\n",
    "char.update({'slot2id' : slot2id})\n",
    "char.update({'id2slot' : id2slot})\n",
    "\n",
    "with open('char_6.17.json', mode='w', encoding='utf-8') as f:\n",
    "    json.dump(char, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans2labelid(vocab, labels, max_sent_len):\n",
    "    labels = [vocab[label] for label in labels]\n",
    "    if len(labels) < max_sent_len:\n",
    "        labels += [0] * (max_sent_len - len(labels))\n",
    "    else:\n",
    "        labels = labels[:max_sent_len]\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(txt_seqs, intent_labels, slot_ners,char2id,intent2id,slot2id,max_sent_len):\n",
    "    dataset_text_labels = []\n",
    "    dataset_intent_labels = []\n",
    "    dataset_ner_labels = []\n",
    "    \n",
    "    for index in range(len(txt_seqs)):\n",
    "        dataset_text_labels.append(trans2labelid(char2id,txt_seqs[index],max_sent_len))\n",
    "        dataset_intent_labels.append([intent2id[intent_labels[index]]])\n",
    "        dataset_ner_labels.append(trans2labelid(slot2id,slot_ners[index],max_sent_len))\n",
    "    dataset_text_labels = np.array(dataset_text_labels)\n",
    "    dataset_intent_labels = np.array(dataset_intent_labels)\n",
    "    dataset_ner_labels = np.array(dataset_ner_labels)\n",
    "    \n",
    "    return dataset_text_labels, dataset_intent_labels, dataset_ner_labels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tarin_seq, train_intent, train_ner =  read_data(train_text, train_intent, train_ner,char2id,intent2id,slot2id,max_sent_len) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_seq, valid_intent, valid_ner =  read_data(valid_text, valid_intent, valid_ner,char2id,intent2id,slot2id,max_sent_len) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Dataset(txt_seqs, dataset_intent_labels, dataset_ner_labels):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(({\n",
    "    \"Input\" : txt_seqs\n",
    "    },\n",
    "    {\n",
    "        \"pre_intent\":dataset_intent_labels,\n",
    "        \n",
    "        \"pre_ner\":dataset_ner_labels\n",
    "    }))\n",
    "    dataset = dataset.batch(params['batch_size'])\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = Dataset(tarin_seq, train_intent, train_ner)\n",
    "valid_dataset = Dataset(valid_seq, valid_intent, valid_ner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "params['intent_num'] = len(intent2id)\n",
    "params['slot_num'] = len(slot2id)\n",
    "params['id2intent'] = id2intent\n",
    "params['id2slot'] = id2slot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_c(x):\n",
    "    val = tf.argmax(x,axis=-1)\n",
    "    val = tf.reshape(val,[params['batch_size'],1])\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ln(c_in):\n",
    "    x = c_in[0]\n",
    "    geta = c_in[1]\n",
    "    #geta = tf.squeeze(geta)\n",
    "#     beta = c_in[2]\n",
    "    #beta = tf.squeeze(beta)\n",
    "    x = tf.keras.layers.LayerNormalization(center=False,scale=False)(x)\n",
    "#     x = geta * x + beta\n",
    "    x = tf.multiply(x,geta)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Input (InputLayer)              [(None, 20)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 20, 32)       16000       Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional (Bidirectional)   [(None, 20, 128), (N 37632       embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "pre_intent (Dense)              (None, 55)           3575        bidirectional[0][1]              \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (64, 1)              0           pre_intent[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (64, 1, 128)         7040        lambda[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (64, 20, 128)        0           bidirectional[0][0]              \n",
      "                                                                 embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "pre_ner (Dense)                 (64, 20, 36)         4644        lambda_1[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 68,891\n",
      "Trainable params: 68,891\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "text_inputs = tf.keras.layers.Input(shape=(20,),name='Input')\n",
    "embed = tf.keras.layers.Embedding(500,32)(text_inputs)\n",
    "bilstm = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64,return_sequences=True, return_state=True))(embed)\n",
    "pre_intent = tf.keras.layers.Dense(params['intent_num'],activation='sigmoid',name = 'pre_intent')(bilstm[1])\n",
    "c_in = tf.keras.layers.Lambda(label_c)(pre_intent)\n",
    "geta_1 = tf.keras.layers.Embedding(55,128)(c_in)\n",
    "# beta_1 = tf.keras.layers.Embedding(55,128)(c_in)\n",
    "# x = tf.keras.layers.Lambda(ln)([bilstm[0],geta_1,beta_1])\n",
    "x = tf.keras.layers.Lambda(ln)([bilstm[0],geta_1])\n",
    "pre_slot = tf.keras.layers.Dense(params['slot_num'],activation='sigmoid',name = 'pre_ner')(x)\n",
    "model = tf.keras.Model(text_inputs,[pre_intent,pre_slot])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = {'pre_intent':'sparse_categorical_crossentropy','pre_ner':'sparse_categorical_crossentropy'}\n",
    "metrics = { 'pre_intent': ['accuracy'],'pre_ner': ['accuracy']}\n",
    "optimizer = tf.keras.optimizers.Adam(params['lr'])\n",
    "model.compile(optimizer, loss=losses, metrics=metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "35/35 [==============================] - 1s 16ms/step - loss: 7.5639 - pre_intent_loss: 4.0138 - pre_ner_loss: 3.5501 - pre_intent_accuracy: 0.0000e+00 - pre_ner_accuracy: 0.5120 - val_loss: 7.5443 - val_pre_intent_loss: 3.9738 - val_pre_ner_loss: 3.5706 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.0612\n",
      "Epoch 2/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 7.3226 - pre_intent_loss: 3.8946 - pre_ner_loss: 3.4280 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.5413 - val_loss: 6.8771 - val_pre_intent_loss: 3.6326 - val_pre_ner_loss: 3.2445 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.6112\n",
      "Epoch 3/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 6.6463 - pre_intent_loss: 3.5929 - pre_ner_loss: 3.0534 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.5797 - val_loss: 6.3113 - val_pre_intent_loss: 3.5430 - val_pre_ner_loss: 2.7683 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.6112\n",
      "Epoch 4/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 5.9589 - pre_intent_loss: 3.4367 - pre_ner_loss: 2.5222 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.5798 - val_loss: 5.6653 - val_pre_intent_loss: 3.5118 - val_pre_ner_loss: 2.1535 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.6112\n",
      "Epoch 5/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 5.3640 - pre_intent_loss: 3.3959 - pre_ner_loss: 1.9681 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.5808 - val_loss: 5.1596 - val_pre_intent_loss: 3.5118 - val_pre_ner_loss: 1.6478 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.6187\n",
      "Epoch 6/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 4.9053 - pre_intent_loss: 3.3815 - pre_ner_loss: 1.5238 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.5990 - val_loss: 4.7745 - val_pre_intent_loss: 3.5117 - val_pre_ner_loss: 1.2629 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.6234\n",
      "Epoch 7/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 4.6000 - pre_intent_loss: 3.3753 - pre_ner_loss: 1.2247 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.6126 - val_loss: 4.5278 - val_pre_intent_loss: 3.5117 - val_pre_ner_loss: 1.0161 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.6380\n",
      "Epoch 8/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 4.3814 - pre_intent_loss: 3.3666 - pre_ner_loss: 1.0148 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.6209 - val_loss: 4.3969 - val_pre_intent_loss: 3.5123 - val_pre_ner_loss: 0.8846 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.6846\n",
      "Epoch 9/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 4.2665 - pre_intent_loss: 3.3599 - pre_ner_loss: 0.9066 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.6790 - val_loss: 4.3012 - val_pre_intent_loss: 3.5109 - val_pre_ner_loss: 0.7903 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.6956\n",
      "Epoch 10/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 4.1681 - pre_intent_loss: 3.3565 - pre_ner_loss: 0.8116 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.7567 - val_loss: 4.2074 - val_pre_intent_loss: 3.5107 - val_pre_ner_loss: 0.6967 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.8057\n",
      "Epoch 11/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 4.0218 - pre_intent_loss: 3.3491 - pre_ner_loss: 0.6727 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.8283 - val_loss: 4.1078 - val_pre_intent_loss: 3.5105 - val_pre_ner_loss: 0.5974 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.8409\n",
      "Epoch 12/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 3.9273 - pre_intent_loss: 3.3477 - pre_ner_loss: 0.5796 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.8537 - val_loss: 4.0350 - val_pre_intent_loss: 3.5092 - val_pre_ner_loss: 0.5258 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.8417\n",
      "Epoch 13/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 3.8410 - pre_intent_loss: 3.3441 - pre_ner_loss: 0.4969 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.8751 - val_loss: 3.9512 - val_pre_intent_loss: 3.5066 - val_pre_ner_loss: 0.4446 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.8862\n",
      "Epoch 14/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 3.8011 - pre_intent_loss: 3.3432 - pre_ner_loss: 0.4579 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.8839 - val_loss: 3.9162 - val_pre_intent_loss: 3.5024 - val_pre_ner_loss: 0.4138 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.8979\n",
      "Epoch 15/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 3.7582 - pre_intent_loss: 3.3371 - pre_ner_loss: 0.4211 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.8937 - val_loss: 3.9067 - val_pre_intent_loss: 3.4974 - val_pre_ner_loss: 0.4092 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9034\n",
      "Epoch 16/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.7193 - pre_intent_loss: 3.3294 - pre_ner_loss: 0.3899 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9018 - val_loss: 3.8544 - val_pre_intent_loss: 3.4976 - val_pre_ner_loss: 0.3568 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9083\n",
      "Epoch 17/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.6972 - pre_intent_loss: 3.3290 - pre_ner_loss: 0.3683 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9111 - val_loss: 3.8504 - val_pre_intent_loss: 3.4975 - val_pre_ner_loss: 0.3529 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9081\n",
      "Epoch 18/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.6704 - pre_intent_loss: 3.3273 - pre_ner_loss: 0.3431 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9151 - val_loss: 3.8196 - val_pre_intent_loss: 3.4948 - val_pre_ner_loss: 0.3248 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9185\n",
      "Epoch 19/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.6504 - pre_intent_loss: 3.3249 - pre_ner_loss: 0.3255 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9198 - val_loss: 3.8036 - val_pre_intent_loss: 3.4896 - val_pre_ner_loss: 0.3139 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9221\n",
      "Epoch 20/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.6230 - pre_intent_loss: 3.3173 - pre_ner_loss: 0.3057 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9256 - val_loss: 3.7848 - val_pre_intent_loss: 3.4861 - val_pre_ner_loss: 0.2987 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9224\n",
      "Epoch 21/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.6065 - pre_intent_loss: 3.3119 - pre_ner_loss: 0.2946 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9269 - val_loss: 3.7725 - val_pre_intent_loss: 3.4831 - val_pre_ner_loss: 0.2894 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9242\n",
      "Epoch 22/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.5884 - pre_intent_loss: 3.3075 - pre_ner_loss: 0.2809 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9275 - val_loss: 3.7590 - val_pre_intent_loss: 3.4774 - val_pre_ner_loss: 0.2816 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9253\n",
      "Epoch 23/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.5701 - pre_intent_loss: 3.3007 - pre_ner_loss: 0.2694 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9289 - val_loss: 3.7352 - val_pre_intent_loss: 3.4666 - val_pre_ner_loss: 0.2687 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9299\n",
      "Epoch 24/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.5449 - pre_intent_loss: 3.2879 - pre_ner_loss: 0.2570 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9317 - val_loss: 3.7128 - val_pre_intent_loss: 3.4507 - val_pre_ner_loss: 0.2621 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9302\n",
      "Epoch 25/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 3.5138 - pre_intent_loss: 3.2665 - pre_ner_loss: 0.2472 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9345 - val_loss: 3.6815 - val_pre_intent_loss: 3.4260 - val_pre_ner_loss: 0.2555 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9299\n",
      "Epoch 26/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.4760 - pre_intent_loss: 3.2328 - pre_ner_loss: 0.2432 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9361 - val_loss: 3.6392 - val_pre_intent_loss: 3.3860 - val_pre_ner_loss: 0.2532 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9318\n",
      "Epoch 27/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.4216 - pre_intent_loss: 3.1782 - pre_ner_loss: 0.2435 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9370 - val_loss: 3.5834 - val_pre_intent_loss: 3.3349 - val_pre_ner_loss: 0.2485 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9346\n",
      "Epoch 28/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.3486 - pre_intent_loss: 3.1124 - pre_ner_loss: 0.2363 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9390 - val_loss: 3.5055 - val_pre_intent_loss: 3.2600 - val_pre_ner_loss: 0.2455 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9352\n",
      "Epoch 29/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.2815 - pre_intent_loss: 3.0504 - pre_ner_loss: 0.2311 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9402 - val_loss: 3.4204 - val_pre_intent_loss: 3.1724 - val_pre_ner_loss: 0.2479 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9362\n",
      "Epoch 30/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.2103 - pre_intent_loss: 2.9823 - pre_ner_loss: 0.2280 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9426 - val_loss: 3.4505 - val_pre_intent_loss: 3.1811 - val_pre_ner_loss: 0.2694 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9357\n",
      "Epoch 31/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.1549 - pre_intent_loss: 2.9168 - pre_ner_loss: 0.2381 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9373 - val_loss: 3.4128 - val_pre_intent_loss: 3.1194 - val_pre_ner_loss: 0.2934 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9312\n",
      "Epoch 32/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.1222 - pre_intent_loss: 2.8767 - pre_ner_loss: 0.2455 - pre_intent_accuracy: 0.2129 - pre_ner_accuracy: 0.9392 - val_loss: 3.2711 - val_pre_intent_loss: 3.0254 - val_pre_ner_loss: 0.2457 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9385\n",
      "Epoch 33/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.1091 - pre_intent_loss: 2.8130 - pre_ner_loss: 0.2961 - pre_intent_accuracy: 0.2147 - pre_ner_accuracy: 0.9376 - val_loss: 3.3188 - val_pre_intent_loss: 3.0706 - val_pre_ner_loss: 0.2482 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9372\n",
      "Epoch 34/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.0577 - pre_intent_loss: 2.8001 - pre_ner_loss: 0.2576 - pre_intent_accuracy: 0.2152 - pre_ner_accuracy: 0.9388 - val_loss: 3.1967 - val_pre_intent_loss: 2.9537 - val_pre_ner_loss: 0.2431 - val_pre_intent_accuracy: 0.1562 - val_pre_ner_accuracy: 0.9419\n",
      "Epoch 35/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.1130 - pre_intent_loss: 2.7139 - pre_ner_loss: 0.3992 - pre_intent_accuracy: 0.2237 - pre_ner_accuracy: 0.9301 - val_loss: 3.6071 - val_pre_intent_loss: 2.8721 - val_pre_ner_loss: 0.7350 - val_pre_intent_accuracy: 0.2292 - val_pre_ner_accuracy: 0.9135\n",
      "Epoch 36/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.4017 - pre_intent_loss: 2.6348 - pre_ner_loss: 0.7669 - pre_intent_accuracy: 0.2848 - pre_ner_accuracy: 0.9054 - val_loss: 3.3507 - val_pre_intent_loss: 2.7883 - val_pre_ner_loss: 0.5624 - val_pre_intent_accuracy: 0.2812 - val_pre_ner_accuracy: 0.9138\n",
      "Epoch 37/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 3.1333 - pre_intent_loss: 2.5578 - pre_ner_loss: 0.5755 - pre_intent_accuracy: 0.3504 - pre_ner_accuracy: 0.9074 - val_loss: 3.1471 - val_pre_intent_loss: 2.7426 - val_pre_ner_loss: 0.4046 - val_pre_intent_accuracy: 0.2812 - val_pre_ner_accuracy: 0.9240\n",
      "Epoch 38/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 2.9662 - pre_intent_loss: 2.5134 - pre_ner_loss: 0.4528 - pre_intent_accuracy: 0.3482 - pre_ner_accuracy: 0.9275 - val_loss: 3.0287 - val_pre_intent_loss: 2.6722 - val_pre_ner_loss: 0.3565 - val_pre_intent_accuracy: 0.3438 - val_pre_ner_accuracy: 0.9323\n",
      "Epoch 39/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.7870 - pre_intent_loss: 2.4443 - pre_ner_loss: 0.3427 - pre_intent_accuracy: 0.3518 - pre_ner_accuracy: 0.9323 - val_loss: 2.9441 - val_pre_intent_loss: 2.6112 - val_pre_ner_loss: 0.3329 - val_pre_intent_accuracy: 0.3490 - val_pre_ner_accuracy: 0.9310\n",
      "Epoch 40/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.7464 - pre_intent_loss: 2.3965 - pre_ner_loss: 0.3499 - pre_intent_accuracy: 0.3612 - pre_ner_accuracy: 0.9358 - val_loss: 2.8947 - val_pre_intent_loss: 2.5892 - val_pre_ner_loss: 0.3055 - val_pre_intent_accuracy: 0.3438 - val_pre_ner_accuracy: 0.9305\n",
      "Epoch 41/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.6841 - pre_intent_loss: 2.3640 - pre_ner_loss: 0.3202 - pre_intent_accuracy: 0.3616 - pre_ner_accuracy: 0.9390 - val_loss: 2.9345 - val_pre_intent_loss: 2.6449 - val_pre_ner_loss: 0.2895 - val_pre_intent_accuracy: 0.2969 - val_pre_ner_accuracy: 0.9312\n",
      "Epoch 42/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.6514 - pre_intent_loss: 2.3513 - pre_ner_loss: 0.3001 - pre_intent_accuracy: 0.3549 - pre_ner_accuracy: 0.9387 - val_loss: 2.8052 - val_pre_intent_loss: 2.5266 - val_pre_ner_loss: 0.2786 - val_pre_intent_accuracy: 0.3490 - val_pre_ner_accuracy: 0.9375\n",
      "Epoch 43/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.5665 - pre_intent_loss: 2.2871 - pre_ner_loss: 0.2795 - pre_intent_accuracy: 0.3714 - pre_ner_accuracy: 0.9387 - val_loss: 2.8267 - val_pre_intent_loss: 2.4597 - val_pre_ner_loss: 0.3670 - val_pre_intent_accuracy: 0.4219 - val_pre_ner_accuracy: 0.9312\n",
      "Epoch 44/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.5093 - pre_intent_loss: 2.2246 - pre_ner_loss: 0.2847 - pre_intent_accuracy: 0.4196 - pre_ner_accuracy: 0.9408 - val_loss: 2.7346 - val_pre_intent_loss: 2.4079 - val_pre_ner_loss: 0.3267 - val_pre_intent_accuracy: 0.4271 - val_pre_ner_accuracy: 0.9354\n",
      "Epoch 45/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.4539 - pre_intent_loss: 2.1762 - pre_ner_loss: 0.2777 - pre_intent_accuracy: 0.4379 - pre_ner_accuracy: 0.9436 - val_loss: 2.6820 - val_pre_intent_loss: 2.4077 - val_pre_ner_loss: 0.2743 - val_pre_intent_accuracy: 0.3594 - val_pre_ner_accuracy: 0.9370\n",
      "Epoch 46/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.4076 - pre_intent_loss: 2.1445 - pre_ner_loss: 0.2630 - pre_intent_accuracy: 0.4491 - pre_ner_accuracy: 0.9413 - val_loss: 2.6094 - val_pre_intent_loss: 2.3485 - val_pre_ner_loss: 0.2610 - val_pre_intent_accuracy: 0.4271 - val_pre_ner_accuracy: 0.9406\n",
      "Epoch 47/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.3437 - pre_intent_loss: 2.0962 - pre_ner_loss: 0.2475 - pre_intent_accuracy: 0.4616 - pre_ner_accuracy: 0.9442 - val_loss: 2.5506 - val_pre_intent_loss: 2.2885 - val_pre_ner_loss: 0.2621 - val_pre_intent_accuracy: 0.4323 - val_pre_ner_accuracy: 0.9396\n",
      "Epoch 48/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.2861 - pre_intent_loss: 2.0460 - pre_ner_loss: 0.2401 - pre_intent_accuracy: 0.4728 - pre_ner_accuracy: 0.9440 - val_loss: 2.5420 - val_pre_intent_loss: 2.2641 - val_pre_ner_loss: 0.2778 - val_pre_intent_accuracy: 0.4375 - val_pre_ner_accuracy: 0.9427\n",
      "Epoch 49/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.2353 - pre_intent_loss: 2.0050 - pre_ner_loss: 0.2303 - pre_intent_accuracy: 0.4817 - pre_ner_accuracy: 0.9463 - val_loss: 2.4717 - val_pre_intent_loss: 2.2218 - val_pre_ner_loss: 0.2499 - val_pre_intent_accuracy: 0.4323 - val_pre_ner_accuracy: 0.9422\n",
      "Epoch 50/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.2031 - pre_intent_loss: 1.9859 - pre_ner_loss: 0.2172 - pre_intent_accuracy: 0.4835 - pre_ner_accuracy: 0.9458 - val_loss: 2.4976 - val_pre_intent_loss: 2.2416 - val_pre_ner_loss: 0.2560 - val_pre_intent_accuracy: 0.4375 - val_pre_ner_accuracy: 0.9424\n",
      "Epoch 51/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.1739 - pre_intent_loss: 1.9568 - pre_ner_loss: 0.2171 - pre_intent_accuracy: 0.5201 - pre_ner_accuracy: 0.9477 - val_loss: 2.3798 - val_pre_intent_loss: 2.1607 - val_pre_ner_loss: 0.2191 - val_pre_intent_accuracy: 0.4375 - val_pre_ner_accuracy: 0.9448\n",
      "Epoch 52/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.1265 - pre_intent_loss: 1.9147 - pre_ner_loss: 0.2119 - pre_intent_accuracy: 0.5393 - pre_ner_accuracy: 0.9494 - val_loss: 2.4308 - val_pre_intent_loss: 2.1551 - val_pre_ner_loss: 0.2757 - val_pre_intent_accuracy: 0.4635 - val_pre_ner_accuracy: 0.9443\n",
      "Epoch 53/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.1024 - pre_intent_loss: 1.8891 - pre_ner_loss: 0.2132 - pre_intent_accuracy: 0.5058 - pre_ner_accuracy: 0.9496 - val_loss: 2.5153 - val_pre_intent_loss: 2.2324 - val_pre_ner_loss: 0.2829 - val_pre_intent_accuracy: 0.4062 - val_pre_ner_accuracy: 0.9388\n",
      "Epoch 54/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.1145 - pre_intent_loss: 1.8806 - pre_ner_loss: 0.2339 - pre_intent_accuracy: 0.4888 - pre_ner_accuracy: 0.9497 - val_loss: 2.3537 - val_pre_intent_loss: 2.1394 - val_pre_ner_loss: 0.2143 - val_pre_intent_accuracy: 0.4323 - val_pre_ner_accuracy: 0.9443\n",
      "Epoch 55/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 2.0526 - pre_intent_loss: 1.8676 - pre_ner_loss: 0.1851 - pre_intent_accuracy: 0.5259 - pre_ner_accuracy: 0.9517 - val_loss: 2.2615 - val_pre_intent_loss: 2.0653 - val_pre_ner_loss: 0.1962 - val_pre_intent_accuracy: 0.4375 - val_pre_ner_accuracy: 0.9464\n",
      "Epoch 56/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.9989 - pre_intent_loss: 1.8131 - pre_ner_loss: 0.1858 - pre_intent_accuracy: 0.5460 - pre_ner_accuracy: 0.9526 - val_loss: 2.2339 - val_pre_intent_loss: 2.0131 - val_pre_ner_loss: 0.2208 - val_pre_intent_accuracy: 0.4635 - val_pre_ner_accuracy: 0.9487\n",
      "Epoch 57/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.9525 - pre_intent_loss: 1.7620 - pre_ner_loss: 0.1905 - pre_intent_accuracy: 0.5433 - pre_ner_accuracy: 0.9530 - val_loss: 2.1909 - val_pre_intent_loss: 1.9860 - val_pre_ner_loss: 0.2049 - val_pre_intent_accuracy: 0.4688 - val_pre_ner_accuracy: 0.9477\n",
      "Epoch 58/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.9313 - pre_intent_loss: 1.7461 - pre_ner_loss: 0.1852 - pre_intent_accuracy: 0.5540 - pre_ner_accuracy: 0.9526 - val_loss: 2.1658 - val_pre_intent_loss: 1.9699 - val_pre_ner_loss: 0.1959 - val_pre_intent_accuracy: 0.4531 - val_pre_ner_accuracy: 0.9484\n",
      "Epoch 59/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.8833 - pre_intent_loss: 1.7084 - pre_ner_loss: 0.1749 - pre_intent_accuracy: 0.5728 - pre_ner_accuracy: 0.9528 - val_loss: 2.1234 - val_pre_intent_loss: 1.9211 - val_pre_ner_loss: 0.2024 - val_pre_intent_accuracy: 0.4740 - val_pre_ner_accuracy: 0.9484\n",
      "Epoch 60/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.8437 - pre_intent_loss: 1.6735 - pre_ner_loss: 0.1702 - pre_intent_accuracy: 0.5772 - pre_ner_accuracy: 0.9551 - val_loss: 2.0943 - val_pre_intent_loss: 1.8978 - val_pre_ner_loss: 0.1965 - val_pre_intent_accuracy: 0.4844 - val_pre_ner_accuracy: 0.9474\n",
      "Epoch 61/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.8092 - pre_intent_loss: 1.6470 - pre_ner_loss: 0.1622 - pre_intent_accuracy: 0.5696 - pre_ner_accuracy: 0.9568 - val_loss: 2.0693 - val_pre_intent_loss: 1.8741 - val_pre_ner_loss: 0.1951 - val_pre_intent_accuracy: 0.4844 - val_pre_ner_accuracy: 0.9505\n",
      "Epoch 62/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.7923 - pre_intent_loss: 1.6259 - pre_ner_loss: 0.1664 - pre_intent_accuracy: 0.5813 - pre_ner_accuracy: 0.9561 - val_loss: 2.0557 - val_pre_intent_loss: 1.8598 - val_pre_ner_loss: 0.1958 - val_pre_intent_accuracy: 0.4792 - val_pre_ner_accuracy: 0.9508\n",
      "Epoch 63/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.7633 - pre_intent_loss: 1.5967 - pre_ner_loss: 0.1667 - pre_intent_accuracy: 0.5920 - pre_ner_accuracy: 0.9564 - val_loss: 2.0294 - val_pre_intent_loss: 1.8314 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.4740 - val_pre_ner_accuracy: 0.9523\n",
      "Epoch 64/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.7281 - pre_intent_loss: 1.5690 - pre_ner_loss: 0.1591 - pre_intent_accuracy: 0.5964 - pre_ner_accuracy: 0.9575 - val_loss: 1.9875 - val_pre_intent_loss: 1.8003 - val_pre_ner_loss: 0.1872 - val_pre_intent_accuracy: 0.4844 - val_pre_ner_accuracy: 0.9510\n",
      "Epoch 65/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.7010 - pre_intent_loss: 1.5442 - pre_ner_loss: 0.1568 - pre_intent_accuracy: 0.5951 - pre_ner_accuracy: 0.9574 - val_loss: 1.9730 - val_pre_intent_loss: 1.7874 - val_pre_ner_loss: 0.1856 - val_pre_intent_accuracy: 0.4792 - val_pre_ner_accuracy: 0.9521\n",
      "Epoch 66/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.7017 - pre_intent_loss: 1.5351 - pre_ner_loss: 0.1665 - pre_intent_accuracy: 0.5893 - pre_ner_accuracy: 0.9569 - val_loss: 2.0383 - val_pre_intent_loss: 1.8465 - val_pre_ner_loss: 0.1919 - val_pre_intent_accuracy: 0.4531 - val_pre_ner_accuracy: 0.9513\n",
      "Epoch 67/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.6935 - pre_intent_loss: 1.5220 - pre_ner_loss: 0.1715 - pre_intent_accuracy: 0.5714 - pre_ner_accuracy: 0.9569 - val_loss: 1.9627 - val_pre_intent_loss: 1.7715 - val_pre_ner_loss: 0.1912 - val_pre_intent_accuracy: 0.4896 - val_pre_ner_accuracy: 0.9518\n",
      "Epoch 68/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.6736 - pre_intent_loss: 1.5064 - pre_ner_loss: 0.1672 - pre_intent_accuracy: 0.6049 - pre_ner_accuracy: 0.9562 - val_loss: 1.9154 - val_pre_intent_loss: 1.7314 - val_pre_ner_loss: 0.1840 - val_pre_intent_accuracy: 0.4896 - val_pre_ner_accuracy: 0.9544\n",
      "Epoch 69/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.6312 - pre_intent_loss: 1.4719 - pre_ner_loss: 0.1593 - pre_intent_accuracy: 0.6103 - pre_ner_accuracy: 0.9567 - val_loss: 1.8578 - val_pre_intent_loss: 1.6777 - val_pre_ner_loss: 0.1802 - val_pre_intent_accuracy: 0.4896 - val_pre_ner_accuracy: 0.9539\n",
      "Epoch 70/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.5887 - pre_intent_loss: 1.4357 - pre_ner_loss: 0.1530 - pre_intent_accuracy: 0.6098 - pre_ner_accuracy: 0.9586 - val_loss: 1.8350 - val_pre_intent_loss: 1.6556 - val_pre_ner_loss: 0.1793 - val_pre_intent_accuracy: 0.5052 - val_pre_ner_accuracy: 0.9547\n",
      "Epoch 71/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.5599 - pre_intent_loss: 1.4037 - pre_ner_loss: 0.1562 - pre_intent_accuracy: 0.6228 - pre_ner_accuracy: 0.9607 - val_loss: 1.8336 - val_pre_intent_loss: 1.6274 - val_pre_ner_loss: 0.2061 - val_pre_intent_accuracy: 0.4948 - val_pre_ner_accuracy: 0.9503\n",
      "Epoch 72/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.5297 - pre_intent_loss: 1.3828 - pre_ner_loss: 0.1469 - pre_intent_accuracy: 0.6179 - pre_ner_accuracy: 0.9597 - val_loss: 1.7895 - val_pre_intent_loss: 1.6130 - val_pre_ner_loss: 0.1765 - val_pre_intent_accuracy: 0.4948 - val_pre_ner_accuracy: 0.9549\n",
      "Epoch 73/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.4999 - pre_intent_loss: 1.3585 - pre_ner_loss: 0.1414 - pre_intent_accuracy: 0.6152 - pre_ner_accuracy: 0.9610 - val_loss: 1.8017 - val_pre_intent_loss: 1.6047 - val_pre_ner_loss: 0.1970 - val_pre_intent_accuracy: 0.5208 - val_pre_ner_accuracy: 0.9516\n",
      "Epoch 74/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.4848 - pre_intent_loss: 1.3340 - pre_ner_loss: 0.1508 - pre_intent_accuracy: 0.6330 - pre_ner_accuracy: 0.9597 - val_loss: 1.7468 - val_pre_intent_loss: 1.5796 - val_pre_ner_loss: 0.1672 - val_pre_intent_accuracy: 0.5156 - val_pre_ner_accuracy: 0.9563\n",
      "Epoch 75/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.4531 - pre_intent_loss: 1.3145 - pre_ner_loss: 0.1386 - pre_intent_accuracy: 0.6366 - pre_ner_accuracy: 0.9615 - val_loss: 1.7456 - val_pre_intent_loss: 1.5619 - val_pre_ner_loss: 0.1838 - val_pre_intent_accuracy: 0.5052 - val_pre_ner_accuracy: 0.9523\n",
      "Epoch 76/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.4667 - pre_intent_loss: 1.3189 - pre_ner_loss: 0.1478 - pre_intent_accuracy: 0.6165 - pre_ner_accuracy: 0.9602 - val_loss: 1.7494 - val_pre_intent_loss: 1.5703 - val_pre_ner_loss: 0.1791 - val_pre_intent_accuracy: 0.5208 - val_pre_ner_accuracy: 0.9544\n",
      "Epoch 77/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.4865 - pre_intent_loss: 1.3478 - pre_ner_loss: 0.1387 - pre_intent_accuracy: 0.6192 - pre_ner_accuracy: 0.9624 - val_loss: 1.7950 - val_pre_intent_loss: 1.5864 - val_pre_ner_loss: 0.2086 - val_pre_intent_accuracy: 0.5781 - val_pre_ner_accuracy: 0.9521\n",
      "Epoch 78/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.4748 - pre_intent_loss: 1.3205 - pre_ner_loss: 0.1544 - pre_intent_accuracy: 0.6612 - pre_ner_accuracy: 0.9587 - val_loss: 1.7332 - val_pre_intent_loss: 1.5026 - val_pre_ner_loss: 0.2306 - val_pre_intent_accuracy: 0.5417 - val_pre_ner_accuracy: 0.9396\n",
      "Epoch 79/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.4345 - pre_intent_loss: 1.2639 - pre_ner_loss: 0.1706 - pre_intent_accuracy: 0.6339 - pre_ner_accuracy: 0.9579 - val_loss: 1.6683 - val_pre_intent_loss: 1.4796 - val_pre_ner_loss: 0.1886 - val_pre_intent_accuracy: 0.5521 - val_pre_ner_accuracy: 0.9536\n",
      "Epoch 80/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.3577 - pre_intent_loss: 1.2298 - pre_ner_loss: 0.1278 - pre_intent_accuracy: 0.6330 - pre_ner_accuracy: 0.9622 - val_loss: 1.6674 - val_pre_intent_loss: 1.4640 - val_pre_ner_loss: 0.2034 - val_pre_intent_accuracy: 0.5833 - val_pre_ner_accuracy: 0.9547\n",
      "Epoch 81/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.3489 - pre_intent_loss: 1.2081 - pre_ner_loss: 0.1408 - pre_intent_accuracy: 0.6759 - pre_ner_accuracy: 0.9633 - val_loss: 1.6239 - val_pre_intent_loss: 1.4227 - val_pre_ner_loss: 0.2012 - val_pre_intent_accuracy: 0.5677 - val_pre_ner_accuracy: 0.9552\n",
      "Epoch 82/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.3113 - pre_intent_loss: 1.1821 - pre_ner_loss: 0.1292 - pre_intent_accuracy: 0.6571 - pre_ner_accuracy: 0.9635 - val_loss: 1.6484 - val_pre_intent_loss: 1.4133 - val_pre_ner_loss: 0.2351 - val_pre_intent_accuracy: 0.5625 - val_pre_ner_accuracy: 0.9513\n",
      "Epoch 83/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.3343 - pre_intent_loss: 1.1687 - pre_ner_loss: 0.1656 - pre_intent_accuracy: 0.6629 - pre_ner_accuracy: 0.9626 - val_loss: 1.6048 - val_pre_intent_loss: 1.4004 - val_pre_ner_loss: 0.2044 - val_pre_intent_accuracy: 0.5833 - val_pre_ner_accuracy: 0.9544\n",
      "Epoch 84/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.3048 - pre_intent_loss: 1.1572 - pre_ner_loss: 0.1475 - pre_intent_accuracy: 0.6920 - pre_ner_accuracy: 0.9622 - val_loss: 1.5539 - val_pre_intent_loss: 1.3853 - val_pre_ner_loss: 0.1685 - val_pre_intent_accuracy: 0.5781 - val_pre_ner_accuracy: 0.9565\n",
      "Epoch 85/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.2692 - pre_intent_loss: 1.1337 - pre_ner_loss: 0.1355 - pre_intent_accuracy: 0.6701 - pre_ner_accuracy: 0.9631 - val_loss: 1.6011 - val_pre_intent_loss: 1.3890 - val_pre_ner_loss: 0.2122 - val_pre_intent_accuracy: 0.5417 - val_pre_ner_accuracy: 0.9557\n",
      "Epoch 86/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.3086 - pre_intent_loss: 1.1319 - pre_ner_loss: 0.1767 - pre_intent_accuracy: 0.7000 - pre_ner_accuracy: 0.9594 - val_loss: 1.5371 - val_pre_intent_loss: 1.3346 - val_pre_ner_loss: 0.2025 - val_pre_intent_accuracy: 0.5625 - val_pre_ner_accuracy: 0.9576\n",
      "Epoch 87/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.2517 - pre_intent_loss: 1.0998 - pre_ner_loss: 0.1519 - pre_intent_accuracy: 0.7004 - pre_ner_accuracy: 0.9639 - val_loss: 1.5572 - val_pre_intent_loss: 1.3280 - val_pre_ner_loss: 0.2293 - val_pre_intent_accuracy: 0.6198 - val_pre_ner_accuracy: 0.9492\n",
      "Epoch 88/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.2526 - pre_intent_loss: 1.0700 - pre_ner_loss: 0.1826 - pre_intent_accuracy: 0.7228 - pre_ner_accuracy: 0.9573 - val_loss: 1.5004 - val_pre_intent_loss: 1.2899 - val_pre_ner_loss: 0.2104 - val_pre_intent_accuracy: 0.6250 - val_pre_ner_accuracy: 0.9563\n",
      "Epoch 89/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.2238 - pre_intent_loss: 1.0497 - pre_ner_loss: 0.1741 - pre_intent_accuracy: 0.7357 - pre_ner_accuracy: 0.9597 - val_loss: 1.5253 - val_pre_intent_loss: 1.2871 - val_pre_ner_loss: 0.2383 - val_pre_intent_accuracy: 0.6667 - val_pre_ner_accuracy: 0.9518\n",
      "Epoch 90/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.1940 - pre_intent_loss: 1.0325 - pre_ner_loss: 0.1615 - pre_intent_accuracy: 0.7478 - pre_ner_accuracy: 0.9607 - val_loss: 1.4489 - val_pre_intent_loss: 1.2487 - val_pre_ner_loss: 0.2002 - val_pre_intent_accuracy: 0.6719 - val_pre_ner_accuracy: 0.9539\n",
      "Epoch 91/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.1748 - pre_intent_loss: 1.0272 - pre_ner_loss: 0.1476 - pre_intent_accuracy: 0.7518 - pre_ner_accuracy: 0.9617 - val_loss: 1.4914 - val_pre_intent_loss: 1.2772 - val_pre_ner_loss: 0.2142 - val_pre_intent_accuracy: 0.6927 - val_pre_ner_accuracy: 0.9510\n",
      "Epoch 92/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.1606 - pre_intent_loss: 1.0161 - pre_ner_loss: 0.1445 - pre_intent_accuracy: 0.7518 - pre_ner_accuracy: 0.9620 - val_loss: 1.4313 - val_pre_intent_loss: 1.2550 - val_pre_ner_loss: 0.1763 - val_pre_intent_accuracy: 0.6458 - val_pre_ner_accuracy: 0.9589\n",
      "Epoch 93/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.1368 - pre_intent_loss: 0.9973 - pre_ner_loss: 0.1395 - pre_intent_accuracy: 0.7594 - pre_ner_accuracy: 0.9653 - val_loss: 1.4218 - val_pre_intent_loss: 1.2077 - val_pre_ner_loss: 0.2141 - val_pre_intent_accuracy: 0.7031 - val_pre_ner_accuracy: 0.9555\n",
      "Epoch 94/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.1122 - pre_intent_loss: 0.9642 - pre_ner_loss: 0.1481 - pre_intent_accuracy: 0.7875 - pre_ner_accuracy: 0.9615 - val_loss: 1.3604 - val_pre_intent_loss: 1.1745 - val_pre_ner_loss: 0.1859 - val_pre_intent_accuracy: 0.7448 - val_pre_ner_accuracy: 0.9560\n",
      "Epoch 95/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.0795 - pre_intent_loss: 0.9406 - pre_ner_loss: 0.1388 - pre_intent_accuracy: 0.7946 - pre_ner_accuracy: 0.9656 - val_loss: 1.3554 - val_pre_intent_loss: 1.1493 - val_pre_ner_loss: 0.2061 - val_pre_intent_accuracy: 0.7396 - val_pre_ner_accuracy: 0.9531\n",
      "Epoch 96/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.0809 - pre_intent_loss: 0.9567 - pre_ner_loss: 0.1241 - pre_intent_accuracy: 0.7746 - pre_ner_accuracy: 0.9650 - val_loss: 1.4302 - val_pre_intent_loss: 1.2188 - val_pre_ner_loss: 0.2114 - val_pre_intent_accuracy: 0.7240 - val_pre_ner_accuracy: 0.9508\n",
      "Epoch 97/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.0904 - pre_intent_loss: 0.9496 - pre_ner_loss: 0.1408 - pre_intent_accuracy: 0.7772 - pre_ner_accuracy: 0.9634 - val_loss: 1.3615 - val_pre_intent_loss: 1.1940 - val_pre_ner_loss: 0.1674 - val_pre_intent_accuracy: 0.6771 - val_pre_ner_accuracy: 0.9576\n",
      "Epoch 98/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.0856 - pre_intent_loss: 0.9619 - pre_ner_loss: 0.1237 - pre_intent_accuracy: 0.7585 - pre_ner_accuracy: 0.9652 - val_loss: 1.3526 - val_pre_intent_loss: 1.1857 - val_pre_ner_loss: 0.1669 - val_pre_intent_accuracy: 0.7031 - val_pre_ner_accuracy: 0.9552\n",
      "Epoch 99/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 1.0664 - pre_intent_loss: 0.9333 - pre_ner_loss: 0.1331 - pre_intent_accuracy: 0.7643 - pre_ner_accuracy: 0.9648 - val_loss: 1.3239 - val_pre_intent_loss: 1.1191 - val_pre_ner_loss: 0.2047 - val_pre_intent_accuracy: 0.7552 - val_pre_ner_accuracy: 0.9510\n",
      "Epoch 100/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.9938 - pre_intent_loss: 0.8680 - pre_ner_loss: 0.1259 - pre_intent_accuracy: 0.8152 - pre_ner_accuracy: 0.9645 - val_loss: 1.2507 - val_pre_intent_loss: 1.0774 - val_pre_ner_loss: 0.1733 - val_pre_intent_accuracy: 0.7552 - val_pre_ner_accuracy: 0.9578\n",
      "Epoch 101/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.9728 - pre_intent_loss: 0.8504 - pre_ner_loss: 0.1223 - pre_intent_accuracy: 0.8161 - pre_ner_accuracy: 0.9670 - val_loss: 1.2320 - val_pre_intent_loss: 1.0591 - val_pre_ner_loss: 0.1729 - val_pre_intent_accuracy: 0.7656 - val_pre_ner_accuracy: 0.9589\n",
      "Epoch 102/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.9580 - pre_intent_loss: 0.8386 - pre_ner_loss: 0.1193 - pre_intent_accuracy: 0.8205 - pre_ner_accuracy: 0.9671 - val_loss: 1.2806 - val_pre_intent_loss: 1.0728 - val_pre_ner_loss: 0.2077 - val_pre_intent_accuracy: 0.7760 - val_pre_ner_accuracy: 0.9549\n",
      "Epoch 103/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.9582 - pre_intent_loss: 0.8418 - pre_ner_loss: 0.1164 - pre_intent_accuracy: 0.8174 - pre_ner_accuracy: 0.9670 - val_loss: 1.2673 - val_pre_intent_loss: 1.0507 - val_pre_ner_loss: 0.2165 - val_pre_intent_accuracy: 0.7656 - val_pre_ner_accuracy: 0.9497\n",
      "Epoch 104/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.9347 - pre_intent_loss: 0.8112 - pre_ner_loss: 0.1235 - pre_intent_accuracy: 0.8308 - pre_ner_accuracy: 0.9661 - val_loss: 1.1976 - val_pre_intent_loss: 1.0112 - val_pre_ner_loss: 0.1864 - val_pre_intent_accuracy: 0.7865 - val_pre_ner_accuracy: 0.9516\n",
      "Epoch 105/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.9011 - pre_intent_loss: 0.7896 - pre_ner_loss: 0.1115 - pre_intent_accuracy: 0.8268 - pre_ner_accuracy: 0.9664 - val_loss: 1.1901 - val_pre_intent_loss: 1.0033 - val_pre_ner_loss: 0.1868 - val_pre_intent_accuracy: 0.7760 - val_pre_ner_accuracy: 0.9536\n",
      "Epoch 106/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.8740 - pre_intent_loss: 0.7691 - pre_ner_loss: 0.1049 - pre_intent_accuracy: 0.8384 - pre_ner_accuracy: 0.9683 - val_loss: 1.1789 - val_pre_intent_loss: 0.9762 - val_pre_ner_loss: 0.2026 - val_pre_intent_accuracy: 0.8073 - val_pre_ner_accuracy: 0.9534\n",
      "Epoch 107/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.8504 - pre_intent_loss: 0.7471 - pre_ner_loss: 0.1034 - pre_intent_accuracy: 0.8375 - pre_ner_accuracy: 0.9691 - val_loss: 1.1454 - val_pre_intent_loss: 0.9570 - val_pre_ner_loss: 0.1884 - val_pre_intent_accuracy: 0.8021 - val_pre_ner_accuracy: 0.9542\n",
      "Epoch 108/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.8310 - pre_intent_loss: 0.7278 - pre_ner_loss: 0.1032 - pre_intent_accuracy: 0.8513 - pre_ner_accuracy: 0.9681 - val_loss: 1.1444 - val_pre_intent_loss: 0.9301 - val_pre_ner_loss: 0.2143 - val_pre_intent_accuracy: 0.8073 - val_pre_ner_accuracy: 0.9516\n",
      "Epoch 109/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.8158 - pre_intent_loss: 0.7073 - pre_ner_loss: 0.1084 - pre_intent_accuracy: 0.8638 - pre_ner_accuracy: 0.9679 - val_loss: 1.1114 - val_pre_intent_loss: 0.9159 - val_pre_ner_loss: 0.1955 - val_pre_intent_accuracy: 0.8125 - val_pre_ner_accuracy: 0.9578\n",
      "Epoch 110/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.8002 - pre_intent_loss: 0.6934 - pre_ner_loss: 0.1069 - pre_intent_accuracy: 0.8710 - pre_ner_accuracy: 0.9681 - val_loss: 1.0669 - val_pre_intent_loss: 0.9056 - val_pre_ner_loss: 0.1613 - val_pre_intent_accuracy: 0.8021 - val_pre_ner_accuracy: 0.9591\n",
      "Epoch 111/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.7816 - pre_intent_loss: 0.6751 - pre_ner_loss: 0.1065 - pre_intent_accuracy: 0.8741 - pre_ner_accuracy: 0.9693 - val_loss: 1.0632 - val_pre_intent_loss: 0.8817 - val_pre_ner_loss: 0.1815 - val_pre_intent_accuracy: 0.8125 - val_pre_ner_accuracy: 0.9581\n",
      "Epoch 112/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.7662 - pre_intent_loss: 0.6587 - pre_ner_loss: 0.1075 - pre_intent_accuracy: 0.8830 - pre_ner_accuracy: 0.9674 - val_loss: 1.0087 - val_pre_intent_loss: 0.8564 - val_pre_ner_loss: 0.1523 - val_pre_intent_accuracy: 0.8125 - val_pre_ner_accuracy: 0.9615\n",
      "Epoch 113/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.7500 - pre_intent_loss: 0.6406 - pre_ner_loss: 0.1094 - pre_intent_accuracy: 0.8866 - pre_ner_accuracy: 0.9685 - val_loss: 0.9955 - val_pre_intent_loss: 0.8397 - val_pre_ner_loss: 0.1558 - val_pre_intent_accuracy: 0.8229 - val_pre_ner_accuracy: 0.9615\n",
      "Epoch 114/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.7378 - pre_intent_loss: 0.6299 - pre_ner_loss: 0.1079 - pre_intent_accuracy: 0.8911 - pre_ner_accuracy: 0.9680 - val_loss: 0.9844 - val_pre_intent_loss: 0.8491 - val_pre_ner_loss: 0.1352 - val_pre_intent_accuracy: 0.7969 - val_pre_ner_accuracy: 0.9607\n",
      "Epoch 115/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.7279 - pre_intent_loss: 0.6200 - pre_ner_loss: 0.1079 - pre_intent_accuracy: 0.8933 - pre_ner_accuracy: 0.9702 - val_loss: 0.9936 - val_pre_intent_loss: 0.8385 - val_pre_ner_loss: 0.1551 - val_pre_intent_accuracy: 0.8281 - val_pre_ner_accuracy: 0.9578\n",
      "Epoch 116/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.7158 - pre_intent_loss: 0.6121 - pre_ner_loss: 0.1036 - pre_intent_accuracy: 0.8946 - pre_ner_accuracy: 0.9664 - val_loss: 0.9432 - val_pre_intent_loss: 0.8066 - val_pre_ner_loss: 0.1365 - val_pre_intent_accuracy: 0.8229 - val_pre_ner_accuracy: 0.9615\n",
      "Epoch 117/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.6994 - pre_intent_loss: 0.5970 - pre_ner_loss: 0.1024 - pre_intent_accuracy: 0.9018 - pre_ner_accuracy: 0.9701 - val_loss: 0.9473 - val_pre_intent_loss: 0.8021 - val_pre_ner_loss: 0.1452 - val_pre_intent_accuracy: 0.8177 - val_pre_ner_accuracy: 0.9604\n",
      "Epoch 118/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.6901 - pre_intent_loss: 0.5884 - pre_ner_loss: 0.1017 - pre_intent_accuracy: 0.9107 - pre_ner_accuracy: 0.9692 - val_loss: 0.9692 - val_pre_intent_loss: 0.8268 - val_pre_ner_loss: 0.1424 - val_pre_intent_accuracy: 0.8281 - val_pre_ner_accuracy: 0.9620\n",
      "Epoch 119/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.6954 - pre_intent_loss: 0.5927 - pre_ner_loss: 0.1027 - pre_intent_accuracy: 0.9009 - pre_ner_accuracy: 0.9716 - val_loss: 0.9289 - val_pre_intent_loss: 0.7904 - val_pre_ner_loss: 0.1384 - val_pre_intent_accuracy: 0.8333 - val_pre_ner_accuracy: 0.9604\n",
      "Epoch 120/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.6676 - pre_intent_loss: 0.5685 - pre_ner_loss: 0.0991 - pre_intent_accuracy: 0.8933 - pre_ner_accuracy: 0.9694 - val_loss: 0.9031 - val_pre_intent_loss: 0.7693 - val_pre_ner_loss: 0.1338 - val_pre_intent_accuracy: 0.8229 - val_pre_ner_accuracy: 0.9615\n",
      "Epoch 121/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.6429 - pre_intent_loss: 0.5458 - pre_ner_loss: 0.0971 - pre_intent_accuracy: 0.9156 - pre_ner_accuracy: 0.9709 - val_loss: 0.9080 - val_pre_intent_loss: 0.7679 - val_pre_ner_loss: 0.1401 - val_pre_intent_accuracy: 0.8438 - val_pre_ner_accuracy: 0.9633\n",
      "Epoch 122/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.6444 - pre_intent_loss: 0.5467 - pre_ner_loss: 0.0977 - pre_intent_accuracy: 0.9219 - pre_ner_accuracy: 0.9708 - val_loss: 0.9236 - val_pre_intent_loss: 0.7844 - val_pre_ner_loss: 0.1391 - val_pre_intent_accuracy: 0.8490 - val_pre_ner_accuracy: 0.9609\n",
      "Epoch 123/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.6524 - pre_intent_loss: 0.5532 - pre_ner_loss: 0.0992 - pre_intent_accuracy: 0.9103 - pre_ner_accuracy: 0.9707 - val_loss: 0.8763 - val_pre_intent_loss: 0.7361 - val_pre_ner_loss: 0.1402 - val_pre_intent_accuracy: 0.8542 - val_pre_ner_accuracy: 0.9599\n",
      "Epoch 124/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.6074 - pre_intent_loss: 0.5111 - pre_ner_loss: 0.0962 - pre_intent_accuracy: 0.9250 - pre_ner_accuracy: 0.9675 - val_loss: 0.8647 - val_pre_intent_loss: 0.7275 - val_pre_ner_loss: 0.1372 - val_pre_intent_accuracy: 0.8594 - val_pre_ner_accuracy: 0.9599\n",
      "Epoch 125/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.5908 - pre_intent_loss: 0.4974 - pre_ner_loss: 0.0933 - pre_intent_accuracy: 0.9277 - pre_ner_accuracy: 0.9709 - val_loss: 0.8469 - val_pre_intent_loss: 0.7149 - val_pre_ner_loss: 0.1319 - val_pre_intent_accuracy: 0.8542 - val_pre_ner_accuracy: 0.9635\n",
      "Epoch 126/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.5756 - pre_intent_loss: 0.4893 - pre_ner_loss: 0.0863 - pre_intent_accuracy: 0.9321 - pre_ner_accuracy: 0.9734 - val_loss: 0.8741 - val_pre_intent_loss: 0.7436 - val_pre_ner_loss: 0.1305 - val_pre_intent_accuracy: 0.8438 - val_pre_ner_accuracy: 0.9612\n",
      "Epoch 127/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.5668 - pre_intent_loss: 0.4805 - pre_ner_loss: 0.0863 - pre_intent_accuracy: 0.9335 - pre_ner_accuracy: 0.9729 - val_loss: 0.8338 - val_pre_intent_loss: 0.6962 - val_pre_ner_loss: 0.1376 - val_pre_intent_accuracy: 0.8802 - val_pre_ner_accuracy: 0.9615\n",
      "Epoch 128/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.5566 - pre_intent_loss: 0.4692 - pre_ner_loss: 0.0874 - pre_intent_accuracy: 0.9339 - pre_ner_accuracy: 0.9722 - val_loss: 0.8385 - val_pre_intent_loss: 0.7118 - val_pre_ner_loss: 0.1267 - val_pre_intent_accuracy: 0.8646 - val_pre_ner_accuracy: 0.9654\n",
      "Epoch 129/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.5390 - pre_intent_loss: 0.4581 - pre_ner_loss: 0.0808 - pre_intent_accuracy: 0.9388 - pre_ner_accuracy: 0.9739 - val_loss: 0.8145 - val_pre_intent_loss: 0.6631 - val_pre_ner_loss: 0.1514 - val_pre_intent_accuracy: 0.8698 - val_pre_ner_accuracy: 0.9641\n",
      "Epoch 130/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.5265 - pre_intent_loss: 0.4437 - pre_ner_loss: 0.0828 - pre_intent_accuracy: 0.9393 - pre_ner_accuracy: 0.9743 - val_loss: 0.8030 - val_pre_intent_loss: 0.6751 - val_pre_ner_loss: 0.1279 - val_pre_intent_accuracy: 0.8646 - val_pre_ner_accuracy: 0.9633\n",
      "Epoch 131/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.5122 - pre_intent_loss: 0.4324 - pre_ner_loss: 0.0798 - pre_intent_accuracy: 0.9348 - pre_ner_accuracy: 0.9734 - val_loss: 0.7663 - val_pre_intent_loss: 0.6340 - val_pre_ner_loss: 0.1322 - val_pre_intent_accuracy: 0.8854 - val_pre_ner_accuracy: 0.9648\n",
      "Epoch 132/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4964 - pre_intent_loss: 0.4184 - pre_ner_loss: 0.0780 - pre_intent_accuracy: 0.9438 - pre_ner_accuracy: 0.9748 - val_loss: 0.7853 - val_pre_intent_loss: 0.6531 - val_pre_ner_loss: 0.1323 - val_pre_intent_accuracy: 0.8750 - val_pre_ner_accuracy: 0.9635\n",
      "Epoch 133/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4884 - pre_intent_loss: 0.4101 - pre_ner_loss: 0.0783 - pre_intent_accuracy: 0.9429 - pre_ner_accuracy: 0.9752 - val_loss: 0.7567 - val_pre_intent_loss: 0.6289 - val_pre_ner_loss: 0.1278 - val_pre_intent_accuracy: 0.8750 - val_pre_ner_accuracy: 0.9643\n",
      "Epoch 134/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4720 - pre_intent_loss: 0.3974 - pre_ner_loss: 0.0746 - pre_intent_accuracy: 0.9473 - pre_ner_accuracy: 0.9751 - val_loss: 0.7757 - val_pre_intent_loss: 0.6492 - val_pre_ner_loss: 0.1265 - val_pre_intent_accuracy: 0.8646 - val_pre_ner_accuracy: 0.9643\n",
      "Epoch 135/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4722 - pre_intent_loss: 0.3967 - pre_ner_loss: 0.0755 - pre_intent_accuracy: 0.9411 - pre_ner_accuracy: 0.9751 - val_loss: 0.7518 - val_pre_intent_loss: 0.6281 - val_pre_ner_loss: 0.1237 - val_pre_intent_accuracy: 0.8698 - val_pre_ner_accuracy: 0.9646\n",
      "Epoch 136/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4576 - pre_intent_loss: 0.3826 - pre_ner_loss: 0.0750 - pre_intent_accuracy: 0.9491 - pre_ner_accuracy: 0.9757 - val_loss: 0.7675 - val_pre_intent_loss: 0.6395 - val_pre_ner_loss: 0.1280 - val_pre_intent_accuracy: 0.8646 - val_pre_ner_accuracy: 0.9661\n",
      "Epoch 137/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4618 - pre_intent_loss: 0.3884 - pre_ner_loss: 0.0734 - pre_intent_accuracy: 0.9424 - pre_ner_accuracy: 0.9756 - val_loss: 0.7522 - val_pre_intent_loss: 0.6288 - val_pre_ner_loss: 0.1234 - val_pre_intent_accuracy: 0.8750 - val_pre_ner_accuracy: 0.9659\n",
      "Epoch 138/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4669 - pre_intent_loss: 0.3919 - pre_ner_loss: 0.0750 - pre_intent_accuracy: 0.9388 - pre_ner_accuracy: 0.9741 - val_loss: 0.7440 - val_pre_intent_loss: 0.6048 - val_pre_ner_loss: 0.1392 - val_pre_intent_accuracy: 0.8802 - val_pre_ner_accuracy: 0.9672\n",
      "Epoch 139/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4686 - pre_intent_loss: 0.3923 - pre_ner_loss: 0.0763 - pre_intent_accuracy: 0.9464 - pre_ner_accuracy: 0.9745 - val_loss: 0.7716 - val_pre_intent_loss: 0.6444 - val_pre_ner_loss: 0.1272 - val_pre_intent_accuracy: 0.8802 - val_pre_ner_accuracy: 0.9654\n",
      "Epoch 140/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4734 - pre_intent_loss: 0.3872 - pre_ner_loss: 0.0861 - pre_intent_accuracy: 0.9366 - pre_ner_accuracy: 0.9709 - val_loss: 0.8589 - val_pre_intent_loss: 0.7180 - val_pre_ner_loss: 0.1408 - val_pre_intent_accuracy: 0.8385 - val_pre_ner_accuracy: 0.9633\n",
      "Epoch 141/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4726 - pre_intent_loss: 0.3858 - pre_ner_loss: 0.0868 - pre_intent_accuracy: 0.9397 - pre_ner_accuracy: 0.9713 - val_loss: 0.7608 - val_pre_intent_loss: 0.6329 - val_pre_ner_loss: 0.1278 - val_pre_intent_accuracy: 0.8594 - val_pre_ner_accuracy: 0.9633\n",
      "Epoch 142/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4603 - pre_intent_loss: 0.3839 - pre_ner_loss: 0.0764 - pre_intent_accuracy: 0.9397 - pre_ner_accuracy: 0.9743 - val_loss: 0.8021 - val_pre_intent_loss: 0.6729 - val_pre_ner_loss: 0.1292 - val_pre_intent_accuracy: 0.8594 - val_pre_ner_accuracy: 0.9667\n",
      "Epoch 143/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.5001 - pre_intent_loss: 0.4240 - pre_ner_loss: 0.0761 - pre_intent_accuracy: 0.9232 - pre_ner_accuracy: 0.9725 - val_loss: 0.7477 - val_pre_intent_loss: 0.6160 - val_pre_ner_loss: 0.1317 - val_pre_intent_accuracy: 0.8698 - val_pre_ner_accuracy: 0.9664\n",
      "Epoch 144/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4656 - pre_intent_loss: 0.3873 - pre_ner_loss: 0.0782 - pre_intent_accuracy: 0.9299 - pre_ner_accuracy: 0.9728 - val_loss: 0.7944 - val_pre_intent_loss: 0.6497 - val_pre_ner_loss: 0.1447 - val_pre_intent_accuracy: 0.8646 - val_pre_ner_accuracy: 0.9607\n",
      "Epoch 145/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4558 - pre_intent_loss: 0.3698 - pre_ner_loss: 0.0859 - pre_intent_accuracy: 0.9406 - pre_ner_accuracy: 0.9726 - val_loss: 0.7055 - val_pre_intent_loss: 0.5798 - val_pre_ner_loss: 0.1257 - val_pre_intent_accuracy: 0.8802 - val_pre_ner_accuracy: 0.9661\n",
      "Epoch 146/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4088 - pre_intent_loss: 0.3356 - pre_ner_loss: 0.0732 - pre_intent_accuracy: 0.9491 - pre_ner_accuracy: 0.9744 - val_loss: 0.6599 - val_pre_intent_loss: 0.5367 - val_pre_ner_loss: 0.1232 - val_pre_intent_accuracy: 0.8750 - val_pre_ner_accuracy: 0.9656\n",
      "Epoch 147/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3808 - pre_intent_loss: 0.3136 - pre_ner_loss: 0.0672 - pre_intent_accuracy: 0.9464 - pre_ner_accuracy: 0.9770 - val_loss: 0.6592 - val_pre_intent_loss: 0.5398 - val_pre_ner_loss: 0.1195 - val_pre_intent_accuracy: 0.8906 - val_pre_ner_accuracy: 0.9656\n",
      "Epoch 148/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3659 - pre_intent_loss: 0.2983 - pre_ner_loss: 0.0676 - pre_intent_accuracy: 0.9598 - pre_ner_accuracy: 0.9775 - val_loss: 0.6392 - val_pre_intent_loss: 0.5171 - val_pre_ner_loss: 0.1221 - val_pre_intent_accuracy: 0.8854 - val_pre_ner_accuracy: 0.9669\n",
      "Epoch 149/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3592 - pre_intent_loss: 0.2930 - pre_ner_loss: 0.0663 - pre_intent_accuracy: 0.9576 - pre_ner_accuracy: 0.9772 - val_loss: 0.6455 - val_pre_intent_loss: 0.5249 - val_pre_ner_loss: 0.1206 - val_pre_intent_accuracy: 0.8906 - val_pre_ner_accuracy: 0.9667\n",
      "Epoch 150/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3473 - pre_intent_loss: 0.2824 - pre_ner_loss: 0.0649 - pre_intent_accuracy: 0.9656 - pre_ner_accuracy: 0.9782 - val_loss: 0.6333 - val_pre_intent_loss: 0.5120 - val_pre_ner_loss: 0.1213 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9672\n",
      "Epoch 151/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3378 - pre_intent_loss: 0.2743 - pre_ner_loss: 0.0634 - pre_intent_accuracy: 0.9665 - pre_ner_accuracy: 0.9772 - val_loss: 0.6254 - val_pre_intent_loss: 0.5066 - val_pre_ner_loss: 0.1189 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9664\n",
      "Epoch 152/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3293 - pre_intent_loss: 0.2671 - pre_ner_loss: 0.0623 - pre_intent_accuracy: 0.9705 - pre_ner_accuracy: 0.9777 - val_loss: 0.6239 - val_pre_intent_loss: 0.5026 - val_pre_ner_loss: 0.1213 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9677\n",
      "Epoch 153/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3248 - pre_intent_loss: 0.2619 - pre_ner_loss: 0.0629 - pre_intent_accuracy: 0.9714 - pre_ner_accuracy: 0.9784 - val_loss: 0.6156 - val_pre_intent_loss: 0.4993 - val_pre_ner_loss: 0.1163 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9677\n",
      "Epoch 154/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3160 - pre_intent_loss: 0.2556 - pre_ner_loss: 0.0603 - pre_intent_accuracy: 0.9723 - pre_ner_accuracy: 0.9783 - val_loss: 0.6120 - val_pre_intent_loss: 0.4924 - val_pre_ner_loss: 0.1196 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 155/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3168 - pre_intent_loss: 0.2562 - pre_ner_loss: 0.0606 - pre_intent_accuracy: 0.9674 - pre_ner_accuracy: 0.9782 - val_loss: 0.6081 - val_pre_intent_loss: 0.4901 - val_pre_ner_loss: 0.1179 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9672\n",
      "Epoch 156/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3065 - pre_intent_loss: 0.2471 - pre_ner_loss: 0.0594 - pre_intent_accuracy: 0.9714 - pre_ner_accuracy: 0.9790 - val_loss: 0.6003 - val_pre_intent_loss: 0.4827 - val_pre_ner_loss: 0.1176 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9677\n",
      "Epoch 157/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3012 - pre_intent_loss: 0.2425 - pre_ner_loss: 0.0587 - pre_intent_accuracy: 0.9705 - pre_ner_accuracy: 0.9783 - val_loss: 0.5908 - val_pre_intent_loss: 0.4765 - val_pre_ner_loss: 0.1144 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9664\n",
      "Epoch 158/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2929 - pre_intent_loss: 0.2351 - pre_ner_loss: 0.0577 - pre_intent_accuracy: 0.9737 - pre_ner_accuracy: 0.9779 - val_loss: 0.5923 - val_pre_intent_loss: 0.4709 - val_pre_ner_loss: 0.1214 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 159/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2874 - pre_intent_loss: 0.2295 - pre_ner_loss: 0.0578 - pre_intent_accuracy: 0.9732 - pre_ner_accuracy: 0.9786 - val_loss: 0.5912 - val_pre_intent_loss: 0.4756 - val_pre_ner_loss: 0.1156 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 160/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2816 - pre_intent_loss: 0.2247 - pre_ner_loss: 0.0569 - pre_intent_accuracy: 0.9737 - pre_ner_accuracy: 0.9790 - val_loss: 0.5815 - val_pre_intent_loss: 0.4646 - val_pre_ner_loss: 0.1168 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 161/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2781 - pre_intent_loss: 0.2202 - pre_ner_loss: 0.0579 - pre_intent_accuracy: 0.9750 - pre_ner_accuracy: 0.9780 - val_loss: 0.5878 - val_pre_intent_loss: 0.4683 - val_pre_ner_loss: 0.1195 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9669\n",
      "Epoch 162/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2740 - pre_intent_loss: 0.2155 - pre_ner_loss: 0.0585 - pre_intent_accuracy: 0.9754 - pre_ner_accuracy: 0.9785 - val_loss: 0.5785 - val_pre_intent_loss: 0.4586 - val_pre_ner_loss: 0.1199 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 163/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2758 - pre_intent_loss: 0.2186 - pre_ner_loss: 0.0572 - pre_intent_accuracy: 0.9710 - pre_ner_accuracy: 0.9783 - val_loss: 0.5826 - val_pre_intent_loss: 0.4659 - val_pre_ner_loss: 0.1166 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9646\n",
      "Epoch 164/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2723 - pre_intent_loss: 0.2136 - pre_ner_loss: 0.0586 - pre_intent_accuracy: 0.9741 - pre_ner_accuracy: 0.9779 - val_loss: 0.5865 - val_pre_intent_loss: 0.4639 - val_pre_ner_loss: 0.1227 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 165/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2757 - pre_intent_loss: 0.2162 - pre_ner_loss: 0.0595 - pre_intent_accuracy: 0.9732 - pre_ner_accuracy: 0.9779 - val_loss: 0.5877 - val_pre_intent_loss: 0.4686 - val_pre_ner_loss: 0.1191 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9672\n",
      "Epoch 166/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2775 - pre_intent_loss: 0.2186 - pre_ner_loss: 0.0589 - pre_intent_accuracy: 0.9696 - pre_ner_accuracy: 0.9781 - val_loss: 0.5831 - val_pre_intent_loss: 0.4547 - val_pre_ner_loss: 0.1284 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9677\n",
      "Epoch 167/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2719 - pre_intent_loss: 0.2135 - pre_ner_loss: 0.0584 - pre_intent_accuracy: 0.9701 - pre_ner_accuracy: 0.9774 - val_loss: 0.5819 - val_pre_intent_loss: 0.4592 - val_pre_ner_loss: 0.1227 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9664\n",
      "Epoch 168/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2698 - pre_intent_loss: 0.2118 - pre_ner_loss: 0.0581 - pre_intent_accuracy: 0.9732 - pre_ner_accuracy: 0.9782 - val_loss: 0.5669 - val_pre_intent_loss: 0.4434 - val_pre_ner_loss: 0.1234 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 169/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2648 - pre_intent_loss: 0.2069 - pre_ner_loss: 0.0580 - pre_intent_accuracy: 0.9679 - pre_ner_accuracy: 0.9781 - val_loss: 0.5647 - val_pre_intent_loss: 0.4450 - val_pre_ner_loss: 0.1196 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9661\n",
      "Epoch 170/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2585 - pre_intent_loss: 0.1990 - pre_ner_loss: 0.0595 - pre_intent_accuracy: 0.9710 - pre_ner_accuracy: 0.9762 - val_loss: 0.5683 - val_pre_intent_loss: 0.4490 - val_pre_ner_loss: 0.1193 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 171/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2505 - pre_intent_loss: 0.1942 - pre_ner_loss: 0.0562 - pre_intent_accuracy: 0.9754 - pre_ner_accuracy: 0.9777 - val_loss: 0.5724 - val_pre_intent_loss: 0.4506 - val_pre_ner_loss: 0.1218 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 172/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2462 - pre_intent_loss: 0.1914 - pre_ner_loss: 0.0548 - pre_intent_accuracy: 0.9759 - pre_ner_accuracy: 0.9793 - val_loss: 0.5606 - val_pre_intent_loss: 0.4386 - val_pre_ner_loss: 0.1219 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9680\n",
      "Epoch 173/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2427 - pre_intent_loss: 0.1868 - pre_ner_loss: 0.0559 - pre_intent_accuracy: 0.9750 - pre_ner_accuracy: 0.9790 - val_loss: 0.5771 - val_pre_intent_loss: 0.4563 - val_pre_ner_loss: 0.1208 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 174/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2529 - pre_intent_loss: 0.1955 - pre_ner_loss: 0.0575 - pre_intent_accuracy: 0.9732 - pre_ner_accuracy: 0.9784 - val_loss: 0.5630 - val_pre_intent_loss: 0.4396 - val_pre_ner_loss: 0.1234 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9672\n",
      "Epoch 175/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2676 - pre_intent_loss: 0.2150 - pre_ner_loss: 0.0526 - pre_intent_accuracy: 0.9665 - pre_ner_accuracy: 0.9802 - val_loss: 0.5541 - val_pre_intent_loss: 0.4363 - val_pre_ner_loss: 0.1178 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 176/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2929 - pre_intent_loss: 0.2340 - pre_ner_loss: 0.0589 - pre_intent_accuracy: 0.9598 - pre_ner_accuracy: 0.9780 - val_loss: 0.7483 - val_pre_intent_loss: 0.6050 - val_pre_ner_loss: 0.1433 - val_pre_intent_accuracy: 0.8438 - val_pre_ner_accuracy: 0.9656\n",
      "Epoch 177/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4381 - pre_intent_loss: 0.3591 - pre_ner_loss: 0.0791 - pre_intent_accuracy: 0.9196 - pre_ner_accuracy: 0.9727 - val_loss: 0.7217 - val_pre_intent_loss: 0.5844 - val_pre_ner_loss: 0.1373 - val_pre_intent_accuracy: 0.8750 - val_pre_ner_accuracy: 0.9651\n",
      "Epoch 178/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3727 - pre_intent_loss: 0.3021 - pre_ner_loss: 0.0705 - pre_intent_accuracy: 0.9348 - pre_ner_accuracy: 0.9748 - val_loss: 0.7160 - val_pre_intent_loss: 0.5652 - val_pre_ner_loss: 0.1507 - val_pre_intent_accuracy: 0.8802 - val_pre_ner_accuracy: 0.9609\n",
      "Epoch 179/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3474 - pre_intent_loss: 0.2792 - pre_ner_loss: 0.0682 - pre_intent_accuracy: 0.9531 - pre_ner_accuracy: 0.9756 - val_loss: 0.5972 - val_pre_intent_loss: 0.4708 - val_pre_ner_loss: 0.1264 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9656\n",
      "Epoch 180/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2727 - pre_intent_loss: 0.2093 - pre_ner_loss: 0.0634 - pre_intent_accuracy: 0.9679 - pre_ner_accuracy: 0.9762 - val_loss: 0.5673 - val_pre_intent_loss: 0.4425 - val_pre_ner_loss: 0.1248 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9661\n",
      "Epoch 181/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2349 - pre_intent_loss: 0.1809 - pre_ner_loss: 0.0540 - pre_intent_accuracy: 0.9746 - pre_ner_accuracy: 0.9791 - val_loss: 0.5432 - val_pre_intent_loss: 0.4263 - val_pre_ner_loss: 0.1169 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 182/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2191 - pre_intent_loss: 0.1680 - pre_ner_loss: 0.0511 - pre_intent_accuracy: 0.9786 - pre_ner_accuracy: 0.9800 - val_loss: 0.5488 - val_pre_intent_loss: 0.4286 - val_pre_ner_loss: 0.1202 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 183/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2091 - pre_intent_loss: 0.1595 - pre_ner_loss: 0.0496 - pre_intent_accuracy: 0.9804 - pre_ner_accuracy: 0.9803 - val_loss: 0.5408 - val_pre_intent_loss: 0.4200 - val_pre_ner_loss: 0.1207 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 184/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2029 - pre_intent_loss: 0.1547 - pre_ner_loss: 0.0482 - pre_intent_accuracy: 0.9799 - pre_ner_accuracy: 0.9809 - val_loss: 0.5368 - val_pre_intent_loss: 0.4171 - val_pre_ner_loss: 0.1196 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 185/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1978 - pre_intent_loss: 0.1506 - pre_ner_loss: 0.0472 - pre_intent_accuracy: 0.9804 - pre_ner_accuracy: 0.9814 - val_loss: 0.5274 - val_pre_intent_loss: 0.4094 - val_pre_ner_loss: 0.1180 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 186/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1926 - pre_intent_loss: 0.1462 - pre_ner_loss: 0.0463 - pre_intent_accuracy: 0.9817 - pre_ner_accuracy: 0.9816 - val_loss: 0.5271 - val_pre_intent_loss: 0.4093 - val_pre_ner_loss: 0.1178 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 187/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1884 - pre_intent_loss: 0.1420 - pre_ner_loss: 0.0465 - pre_intent_accuracy: 0.9821 - pre_ner_accuracy: 0.9820 - val_loss: 0.5245 - val_pre_intent_loss: 0.4064 - val_pre_ner_loss: 0.1181 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 188/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1840 - pre_intent_loss: 0.1388 - pre_ner_loss: 0.0452 - pre_intent_accuracy: 0.9826 - pre_ner_accuracy: 0.9817 - val_loss: 0.5160 - val_pre_intent_loss: 0.3971 - val_pre_ner_loss: 0.1189 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9680\n",
      "Epoch 189/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1904 - pre_intent_loss: 0.1452 - pre_ner_loss: 0.0452 - pre_intent_accuracy: 0.9817 - pre_ner_accuracy: 0.9819 - val_loss: 0.5465 - val_pre_intent_loss: 0.4285 - val_pre_ner_loss: 0.1180 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 190/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2002 - pre_intent_loss: 0.1552 - pre_ner_loss: 0.0449 - pre_intent_accuracy: 0.9786 - pre_ner_accuracy: 0.9821 - val_loss: 0.5100 - val_pre_intent_loss: 0.3930 - val_pre_ner_loss: 0.1170 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 191/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1865 - pre_intent_loss: 0.1422 - pre_ner_loss: 0.0443 - pre_intent_accuracy: 0.9817 - pre_ner_accuracy: 0.9819 - val_loss: 0.5074 - val_pre_intent_loss: 0.3909 - val_pre_ner_loss: 0.1165 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 192/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1814 - pre_intent_loss: 0.1365 - pre_ner_loss: 0.0449 - pre_intent_accuracy: 0.9821 - pre_ner_accuracy: 0.9816 - val_loss: 0.5132 - val_pre_intent_loss: 0.3882 - val_pre_ner_loss: 0.1250 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 193/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1766 - pre_intent_loss: 0.1321 - pre_ner_loss: 0.0445 - pre_intent_accuracy: 0.9839 - pre_ner_accuracy: 0.9825 - val_loss: 0.5068 - val_pre_intent_loss: 0.3893 - val_pre_ner_loss: 0.1174 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 194/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1717 - pre_intent_loss: 0.1279 - pre_ner_loss: 0.0438 - pre_intent_accuracy: 0.9839 - pre_ner_accuracy: 0.9821 - val_loss: 0.5052 - val_pre_intent_loss: 0.3855 - val_pre_ner_loss: 0.1197 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 195/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1664 - pre_intent_loss: 0.1231 - pre_ner_loss: 0.0433 - pre_intent_accuracy: 0.9848 - pre_ner_accuracy: 0.9824 - val_loss: 0.5030 - val_pre_intent_loss: 0.3851 - val_pre_ner_loss: 0.1180 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 196/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1622 - pre_intent_loss: 0.1189 - pre_ner_loss: 0.0433 - pre_intent_accuracy: 0.9848 - pre_ner_accuracy: 0.9824 - val_loss: 0.4976 - val_pre_intent_loss: 0.3772 - val_pre_ner_loss: 0.1204 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 197/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1578 - pre_intent_loss: 0.1153 - pre_ner_loss: 0.0425 - pre_intent_accuracy: 0.9853 - pre_ner_accuracy: 0.9823 - val_loss: 0.4965 - val_pre_intent_loss: 0.3763 - val_pre_ner_loss: 0.1203 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 198/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1554 - pre_intent_loss: 0.1133 - pre_ner_loss: 0.0421 - pre_intent_accuracy: 0.9848 - pre_ner_accuracy: 0.9829 - val_loss: 0.4931 - val_pre_intent_loss: 0.3763 - val_pre_ner_loss: 0.1168 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 199/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1532 - pre_intent_loss: 0.1112 - pre_ner_loss: 0.0420 - pre_intent_accuracy: 0.9848 - pre_ner_accuracy: 0.9826 - val_loss: 0.4891 - val_pre_intent_loss: 0.3716 - val_pre_ner_loss: 0.1175 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9680\n",
      "Epoch 200/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1508 - pre_intent_loss: 0.1090 - pre_ner_loss: 0.0418 - pre_intent_accuracy: 0.9857 - pre_ner_accuracy: 0.9829 - val_loss: 0.4844 - val_pre_intent_loss: 0.3670 - val_pre_ner_loss: 0.1174 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 201/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1477 - pre_intent_loss: 0.1065 - pre_ner_loss: 0.0412 - pre_intent_accuracy: 0.9862 - pre_ner_accuracy: 0.9833 - val_loss: 0.4917 - val_pre_intent_loss: 0.3689 - val_pre_ner_loss: 0.1227 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 202/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1453 - pre_intent_loss: 0.1042 - pre_ner_loss: 0.0411 - pre_intent_accuracy: 0.9862 - pre_ner_accuracy: 0.9827 - val_loss: 0.4767 - val_pre_intent_loss: 0.3576 - val_pre_ner_loss: 0.1191 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 203/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1440 - pre_intent_loss: 0.1025 - pre_ner_loss: 0.0414 - pre_intent_accuracy: 0.9862 - pre_ner_accuracy: 0.9832 - val_loss: 0.4889 - val_pre_intent_loss: 0.3723 - val_pre_ner_loss: 0.1165 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 204/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1446 - pre_intent_loss: 0.1036 - pre_ner_loss: 0.0411 - pre_intent_accuracy: 0.9857 - pre_ner_accuracy: 0.9828 - val_loss: 0.4811 - val_pre_intent_loss: 0.3616 - val_pre_ner_loss: 0.1195 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 205/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1390 - pre_intent_loss: 0.0989 - pre_ner_loss: 0.0401 - pre_intent_accuracy: 0.9857 - pre_ner_accuracy: 0.9838 - val_loss: 0.4815 - val_pre_intent_loss: 0.3604 - val_pre_ner_loss: 0.1211 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 206/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1358 - pre_intent_loss: 0.0965 - pre_ner_loss: 0.0392 - pre_intent_accuracy: 0.9862 - pre_ner_accuracy: 0.9841 - val_loss: 0.4765 - val_pre_intent_loss: 0.3565 - val_pre_ner_loss: 0.1200 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 207/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1333 - pre_intent_loss: 0.0945 - pre_ner_loss: 0.0388 - pre_intent_accuracy: 0.9862 - pre_ner_accuracy: 0.9839 - val_loss: 0.4747 - val_pre_intent_loss: 0.3576 - val_pre_ner_loss: 0.1171 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 208/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1315 - pre_intent_loss: 0.0928 - pre_ner_loss: 0.0387 - pre_intent_accuracy: 0.9862 - pre_ner_accuracy: 0.9840 - val_loss: 0.4686 - val_pre_intent_loss: 0.3511 - val_pre_ner_loss: 0.1175 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 209/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1298 - pre_intent_loss: 0.0909 - pre_ner_loss: 0.0390 - pre_intent_accuracy: 0.9866 - pre_ner_accuracy: 0.9841 - val_loss: 0.4753 - val_pre_intent_loss: 0.3541 - val_pre_ner_loss: 0.1212 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 210/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1279 - pre_intent_loss: 0.0895 - pre_ner_loss: 0.0383 - pre_intent_accuracy: 0.9857 - pre_ner_accuracy: 0.9838 - val_loss: 0.4691 - val_pre_intent_loss: 0.3442 - val_pre_ner_loss: 0.1249 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 211/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1270 - pre_intent_loss: 0.0874 - pre_ner_loss: 0.0396 - pre_intent_accuracy: 0.9862 - pre_ner_accuracy: 0.9838 - val_loss: 0.4791 - val_pre_intent_loss: 0.3589 - val_pre_ner_loss: 0.1202 - val_pre_intent_accuracy: 0.9010 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 212/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1287 - pre_intent_loss: 0.0859 - pre_ner_loss: 0.0428 - pre_intent_accuracy: 0.9871 - pre_ner_accuracy: 0.9826 - val_loss: 0.4566 - val_pre_intent_loss: 0.3372 - val_pre_ner_loss: 0.1194 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 213/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1251 - pre_intent_loss: 0.0844 - pre_ner_loss: 0.0406 - pre_intent_accuracy: 0.9879 - pre_ner_accuracy: 0.9838 - val_loss: 0.4854 - val_pre_intent_loss: 0.3567 - val_pre_ner_loss: 0.1286 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 214/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1217 - pre_intent_loss: 0.0825 - pre_ner_loss: 0.0392 - pre_intent_accuracy: 0.9875 - pre_ner_accuracy: 0.9832 - val_loss: 0.4617 - val_pre_intent_loss: 0.3408 - val_pre_ner_loss: 0.1209 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 215/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1191 - pre_intent_loss: 0.0810 - pre_ner_loss: 0.0381 - pre_intent_accuracy: 0.9884 - pre_ner_accuracy: 0.9840 - val_loss: 0.4756 - val_pre_intent_loss: 0.3574 - val_pre_ner_loss: 0.1182 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 216/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1189 - pre_intent_loss: 0.0801 - pre_ner_loss: 0.0387 - pre_intent_accuracy: 0.9897 - pre_ner_accuracy: 0.9844 - val_loss: 0.4689 - val_pre_intent_loss: 0.3495 - val_pre_ner_loss: 0.1194 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 217/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1184 - pre_intent_loss: 0.0808 - pre_ner_loss: 0.0375 - pre_intent_accuracy: 0.9902 - pre_ner_accuracy: 0.9842 - val_loss: 0.5003 - val_pre_intent_loss: 0.3733 - val_pre_ner_loss: 0.1270 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 218/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1359 - pre_intent_loss: 0.0955 - pre_ner_loss: 0.0405 - pre_intent_accuracy: 0.9866 - pre_ner_accuracy: 0.9839 - val_loss: 0.5106 - val_pre_intent_loss: 0.3873 - val_pre_ner_loss: 0.1233 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 219/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1357 - pre_intent_loss: 0.0937 - pre_ner_loss: 0.0420 - pre_intent_accuracy: 0.9839 - pre_ner_accuracy: 0.9823 - val_loss: 0.5102 - val_pre_intent_loss: 0.3895 - val_pre_ner_loss: 0.1207 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 220/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1399 - pre_intent_loss: 0.0975 - pre_ner_loss: 0.0424 - pre_intent_accuracy: 0.9835 - pre_ner_accuracy: 0.9831 - val_loss: 0.4780 - val_pre_intent_loss: 0.3489 - val_pre_ner_loss: 0.1291 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 221/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1437 - pre_intent_loss: 0.0941 - pre_ner_loss: 0.0496 - pre_intent_accuracy: 0.9848 - pre_ner_accuracy: 0.9810 - val_loss: 0.4500 - val_pre_intent_loss: 0.3243 - val_pre_ner_loss: 0.1257 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9667\n",
      "Epoch 222/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1307 - pre_intent_loss: 0.0872 - pre_ner_loss: 0.0435 - pre_intent_accuracy: 0.9835 - pre_ner_accuracy: 0.9825 - val_loss: 0.4490 - val_pre_intent_loss: 0.3241 - val_pre_ner_loss: 0.1249 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 223/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1265 - pre_intent_loss: 0.0841 - pre_ner_loss: 0.0424 - pre_intent_accuracy: 0.9897 - pre_ner_accuracy: 0.9822 - val_loss: 0.4738 - val_pre_intent_loss: 0.3506 - val_pre_ner_loss: 0.1232 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9667\n",
      "Epoch 224/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1288 - pre_intent_loss: 0.0838 - pre_ner_loss: 0.0451 - pre_intent_accuracy: 0.9857 - pre_ner_accuracy: 0.9826 - val_loss: 0.4555 - val_pre_intent_loss: 0.3327 - val_pre_ner_loss: 0.1229 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 225/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1122 - pre_intent_loss: 0.0741 - pre_ner_loss: 0.0381 - pre_intent_accuracy: 0.9879 - pre_ner_accuracy: 0.9837 - val_loss: 0.4557 - val_pre_intent_loss: 0.3325 - val_pre_ner_loss: 0.1232 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 226/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1045 - pre_intent_loss: 0.0678 - pre_ner_loss: 0.0367 - pre_intent_accuracy: 0.9884 - pre_ner_accuracy: 0.9846 - val_loss: 0.4442 - val_pre_intent_loss: 0.3281 - val_pre_ner_loss: 0.1162 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 227/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1009 - pre_intent_loss: 0.0654 - pre_ner_loss: 0.0355 - pre_intent_accuracy: 0.9902 - pre_ner_accuracy: 0.9852 - val_loss: 0.4433 - val_pre_intent_loss: 0.3240 - val_pre_ner_loss: 0.1193 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 228/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0988 - pre_intent_loss: 0.0639 - pre_ner_loss: 0.0349 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9849 - val_loss: 0.4466 - val_pre_intent_loss: 0.3235 - val_pre_ner_loss: 0.1231 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 229/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0973 - pre_intent_loss: 0.0624 - pre_ner_loss: 0.0349 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9854 - val_loss: 0.4389 - val_pre_intent_loss: 0.3221 - val_pre_ner_loss: 0.1167 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9716\n",
      "Epoch 230/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0961 - pre_intent_loss: 0.0612 - pre_ner_loss: 0.0349 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9848 - val_loss: 0.4405 - val_pre_intent_loss: 0.3193 - val_pre_ner_loss: 0.1211 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 231/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0942 - pre_intent_loss: 0.0600 - pre_ner_loss: 0.0342 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9860 - val_loss: 0.4401 - val_pre_intent_loss: 0.3227 - val_pre_ner_loss: 0.1174 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 232/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0940 - pre_intent_loss: 0.0589 - pre_ner_loss: 0.0351 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9842 - val_loss: 0.4382 - val_pre_intent_loss: 0.3158 - val_pre_ner_loss: 0.1225 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 233/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0923 - pre_intent_loss: 0.0581 - pre_ner_loss: 0.0342 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9856 - val_loss: 0.4374 - val_pre_intent_loss: 0.3192 - val_pre_ner_loss: 0.1181 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 234/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0935 - pre_intent_loss: 0.0587 - pre_ner_loss: 0.0348 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9843 - val_loss: 0.4373 - val_pre_intent_loss: 0.3155 - val_pre_ner_loss: 0.1218 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 235/500\n",
      "35/35 [==============================] - 0s 10ms/step - loss: 0.0905 - pre_intent_loss: 0.0567 - pre_ner_loss: 0.0337 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9856 - val_loss: 0.4452 - val_pre_intent_loss: 0.3244 - val_pre_ner_loss: 0.1208 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 236/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0889 - pre_intent_loss: 0.0551 - pre_ner_loss: 0.0338 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9847 - val_loss: 0.4383 - val_pre_intent_loss: 0.3177 - val_pre_ner_loss: 0.1206 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9727\n",
      "Epoch 237/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0872 - pre_intent_loss: 0.0543 - pre_ner_loss: 0.0329 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9857 - val_loss: 0.4473 - val_pre_intent_loss: 0.3249 - val_pre_ner_loss: 0.1224 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9716\n",
      "Epoch 238/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0861 - pre_intent_loss: 0.0533 - pre_ner_loss: 0.0329 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9852 - val_loss: 0.4368 - val_pre_intent_loss: 0.3147 - val_pre_ner_loss: 0.1221 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 239/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0853 - pre_intent_loss: 0.0525 - pre_ner_loss: 0.0328 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9860 - val_loss: 0.4405 - val_pre_intent_loss: 0.3206 - val_pre_ner_loss: 0.1199 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 240/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0848 - pre_intent_loss: 0.0514 - pre_ner_loss: 0.0334 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9849 - val_loss: 0.4406 - val_pre_intent_loss: 0.3151 - val_pre_ner_loss: 0.1255 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9716\n",
      "Epoch 241/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0844 - pre_intent_loss: 0.0505 - pre_ner_loss: 0.0339 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9856 - val_loss: 0.4376 - val_pre_intent_loss: 0.3190 - val_pre_ner_loss: 0.1186 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 242/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0869 - pre_intent_loss: 0.0499 - pre_ner_loss: 0.0371 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9841 - val_loss: 0.4364 - val_pre_intent_loss: 0.3091 - val_pre_ner_loss: 0.1273 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 243/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0831 - pre_intent_loss: 0.0491 - pre_ner_loss: 0.0340 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9851 - val_loss: 0.4420 - val_pre_intent_loss: 0.3164 - val_pre_ner_loss: 0.1256 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 244/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0806 - pre_intent_loss: 0.0481 - pre_ner_loss: 0.0325 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9856 - val_loss: 0.4360 - val_pre_intent_loss: 0.3127 - val_pre_ner_loss: 0.1234 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 245/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0790 - pre_intent_loss: 0.0473 - pre_ner_loss: 0.0317 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9854 - val_loss: 0.4392 - val_pre_intent_loss: 0.3131 - val_pre_ner_loss: 0.1260 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 246/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0775 - pre_intent_loss: 0.0467 - pre_ner_loss: 0.0308 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9868 - val_loss: 0.4346 - val_pre_intent_loss: 0.3114 - val_pre_ner_loss: 0.1232 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9727\n",
      "Epoch 247/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0762 - pre_intent_loss: 0.0458 - pre_ner_loss: 0.0304 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9864 - val_loss: 0.4393 - val_pre_intent_loss: 0.3157 - val_pre_ner_loss: 0.1236 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 248/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0751 - pre_intent_loss: 0.0451 - pre_ner_loss: 0.0300 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9867 - val_loss: 0.4358 - val_pre_intent_loss: 0.3089 - val_pre_ner_loss: 0.1269 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 249/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0743 - pre_intent_loss: 0.0445 - pre_ner_loss: 0.0298 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9863 - val_loss: 0.4416 - val_pre_intent_loss: 0.3173 - val_pre_ner_loss: 0.1243 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9716\n",
      "Epoch 250/500\n",
      "35/35 [==============================] - 0s 9ms/step - loss: 0.0763 - pre_intent_loss: 0.0439 - pre_ner_loss: 0.0324 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9858 - val_loss: 0.4415 - val_pre_intent_loss: 0.3140 - val_pre_ner_loss: 0.1275 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 251/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0760 - pre_intent_loss: 0.0437 - pre_ner_loss: 0.0322 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9855 - val_loss: 0.4340 - val_pre_intent_loss: 0.3086 - val_pre_ner_loss: 0.1254 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 252/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0753 - pre_intent_loss: 0.0427 - pre_ner_loss: 0.0326 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9858 - val_loss: 0.4486 - val_pre_intent_loss: 0.3212 - val_pre_ner_loss: 0.1275 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 253/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0796 - pre_intent_loss: 0.0426 - pre_ner_loss: 0.0371 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9849 - val_loss: 0.4236 - val_pre_intent_loss: 0.2951 - val_pre_ner_loss: 0.1285 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 254/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0751 - pre_intent_loss: 0.0418 - pre_ner_loss: 0.0332 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9854 - val_loss: 0.4534 - val_pre_intent_loss: 0.3249 - val_pre_ner_loss: 0.1285 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 255/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0798 - pre_intent_loss: 0.0443 - pre_ner_loss: 0.0355 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9843 - val_loss: 0.4258 - val_pre_intent_loss: 0.3059 - val_pre_ner_loss: 0.1199 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 256/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1022 - pre_intent_loss: 0.0669 - pre_ner_loss: 0.0353 - pre_intent_accuracy: 0.9790 - pre_ner_accuracy: 0.9848 - val_loss: 0.7793 - val_pre_intent_loss: 0.6205 - val_pre_ner_loss: 0.1588 - val_pre_intent_accuracy: 0.8490 - val_pre_ner_accuracy: 0.9589\n",
      "Epoch 257/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2502 - pre_intent_loss: 0.1952 - pre_ner_loss: 0.0550 - pre_intent_accuracy: 0.9482 - pre_ner_accuracy: 0.9815 - val_loss: 0.7651 - val_pre_intent_loss: 0.6208 - val_pre_ner_loss: 0.1443 - val_pre_intent_accuracy: 0.8646 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 258/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.4686 - pre_intent_loss: 0.4081 - pre_ner_loss: 0.0605 - pre_intent_accuracy: 0.9004 - pre_ner_accuracy: 0.9796 - val_loss: 0.7732 - val_pre_intent_loss: 0.6051 - val_pre_ner_loss: 0.1681 - val_pre_intent_accuracy: 0.7969 - val_pre_ner_accuracy: 0.9594\n",
      "Epoch 259/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3807 - pre_intent_loss: 0.3057 - pre_ner_loss: 0.0750 - pre_intent_accuracy: 0.9201 - pre_ner_accuracy: 0.9741 - val_loss: 0.7804 - val_pre_intent_loss: 0.6226 - val_pre_ner_loss: 0.1579 - val_pre_intent_accuracy: 0.8490 - val_pre_ner_accuracy: 0.9604\n",
      "Epoch 260/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.3473 - pre_intent_loss: 0.2817 - pre_ner_loss: 0.0656 - pre_intent_accuracy: 0.9362 - pre_ner_accuracy: 0.9773 - val_loss: 0.5588 - val_pre_intent_loss: 0.4095 - val_pre_ner_loss: 0.1493 - val_pre_intent_accuracy: 0.9062 - val_pre_ner_accuracy: 0.9638\n",
      "Epoch 261/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2093 - pre_intent_loss: 0.1598 - pre_ner_loss: 0.0495 - pre_intent_accuracy: 0.9567 - pre_ner_accuracy: 0.9800 - val_loss: 0.4334 - val_pre_intent_loss: 0.3022 - val_pre_ner_loss: 0.1313 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 262/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1350 - pre_intent_loss: 0.0947 - pre_ner_loss: 0.0403 - pre_intent_accuracy: 0.9808 - pre_ner_accuracy: 0.9829 - val_loss: 0.3676 - val_pre_intent_loss: 0.2461 - val_pre_ner_loss: 0.1215 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 263/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1084 - pre_intent_loss: 0.0736 - pre_ner_loss: 0.0348 - pre_intent_accuracy: 0.9866 - pre_ner_accuracy: 0.9846 - val_loss: 0.3742 - val_pre_intent_loss: 0.2519 - val_pre_ner_loss: 0.1223 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 264/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0915 - pre_intent_loss: 0.0586 - pre_ner_loss: 0.0329 - pre_intent_accuracy: 0.9911 - pre_ner_accuracy: 0.9856 - val_loss: 0.3890 - val_pre_intent_loss: 0.2665 - val_pre_ner_loss: 0.1225 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 265/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0879 - pre_intent_loss: 0.0562 - pre_ner_loss: 0.0317 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9856 - val_loss: 0.3670 - val_pre_intent_loss: 0.2482 - val_pre_ner_loss: 0.1187 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 266/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0836 - pre_intent_loss: 0.0521 - pre_ner_loss: 0.0316 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9861 - val_loss: 0.3838 - val_pre_intent_loss: 0.2642 - val_pre_ner_loss: 0.1195 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9727\n",
      "Epoch 267/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0806 - pre_intent_loss: 0.0498 - pre_ner_loss: 0.0308 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9859 - val_loss: 0.3847 - val_pre_intent_loss: 0.2653 - val_pre_ner_loss: 0.1194 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 268/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0776 - pre_intent_loss: 0.0472 - pre_ner_loss: 0.0304 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9863 - val_loss: 0.3851 - val_pre_intent_loss: 0.2667 - val_pre_ner_loss: 0.1184 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 269/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0746 - pre_intent_loss: 0.0451 - pre_ner_loss: 0.0295 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9865 - val_loss: 0.3892 - val_pre_intent_loss: 0.2691 - val_pre_ner_loss: 0.1202 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 270/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0723 - pre_intent_loss: 0.0432 - pre_ner_loss: 0.0291 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9867 - val_loss: 0.3907 - val_pre_intent_loss: 0.2711 - val_pre_ner_loss: 0.1196 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 271/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0706 - pre_intent_loss: 0.0420 - pre_ner_loss: 0.0286 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9870 - val_loss: 0.3883 - val_pre_intent_loss: 0.2691 - val_pre_ner_loss: 0.1191 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9716\n",
      "Epoch 272/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0689 - pre_intent_loss: 0.0404 - pre_ner_loss: 0.0284 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9869 - val_loss: 0.3926 - val_pre_intent_loss: 0.2720 - val_pre_ner_loss: 0.1206 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9727\n",
      "Epoch 273/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0675 - pre_intent_loss: 0.0393 - pre_ner_loss: 0.0281 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9872 - val_loss: 0.3954 - val_pre_intent_loss: 0.2761 - val_pre_ner_loss: 0.1193 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 274/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0664 - pre_intent_loss: 0.0382 - pre_ner_loss: 0.0282 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9867 - val_loss: 0.3965 - val_pre_intent_loss: 0.2755 - val_pre_ner_loss: 0.1210 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 275/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0653 - pre_intent_loss: 0.0375 - pre_ner_loss: 0.0279 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9873 - val_loss: 0.4000 - val_pre_intent_loss: 0.2799 - val_pre_ner_loss: 0.1200 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 276/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0644 - pre_intent_loss: 0.0365 - pre_ner_loss: 0.0279 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9866 - val_loss: 0.4000 - val_pre_intent_loss: 0.2786 - val_pre_ner_loss: 0.1214 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 277/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0633 - pre_intent_loss: 0.0358 - pre_ner_loss: 0.0275 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9873 - val_loss: 0.4025 - val_pre_intent_loss: 0.2817 - val_pre_ner_loss: 0.1208 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 278/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0627 - pre_intent_loss: 0.0351 - pre_ner_loss: 0.0276 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9867 - val_loss: 0.4006 - val_pre_intent_loss: 0.2793 - val_pre_ner_loss: 0.1213 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9727\n",
      "Epoch 279/500\n",
      "35/35 [==============================] - 0s 10ms/step - loss: 0.0619 - pre_intent_loss: 0.0348 - pre_ner_loss: 0.0271 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9873 - val_loss: 0.3636 - val_pre_intent_loss: 0.2425 - val_pre_ner_loss: 0.1211 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 280/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0616 - pre_intent_loss: 0.0344 - pre_ner_loss: 0.0273 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9869 - val_loss: 0.3920 - val_pre_intent_loss: 0.2705 - val_pre_ner_loss: 0.1215 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 281/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0602 - pre_intent_loss: 0.0336 - pre_ner_loss: 0.0266 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9876 - val_loss: 0.4052 - val_pre_intent_loss: 0.2839 - val_pre_ner_loss: 0.1214 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 282/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0596 - pre_intent_loss: 0.0330 - pre_ner_loss: 0.0265 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9873 - val_loss: 0.4060 - val_pre_intent_loss: 0.2843 - val_pre_ner_loss: 0.1216 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 283/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0589 - pre_intent_loss: 0.0326 - pre_ner_loss: 0.0263 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9877 - val_loss: 0.4114 - val_pre_intent_loss: 0.2891 - val_pre_ner_loss: 0.1223 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 284/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0584 - pre_intent_loss: 0.0321 - pre_ner_loss: 0.0263 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9873 - val_loss: 0.4103 - val_pre_intent_loss: 0.2881 - val_pre_ner_loss: 0.1222 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9727\n",
      "Epoch 285/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0577 - pre_intent_loss: 0.0316 - pre_ner_loss: 0.0260 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9877 - val_loss: 0.4157 - val_pre_intent_loss: 0.2926 - val_pre_ner_loss: 0.1231 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 286/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0573 - pre_intent_loss: 0.0312 - pre_ner_loss: 0.0261 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9873 - val_loss: 0.4141 - val_pre_intent_loss: 0.2912 - val_pre_ner_loss: 0.1229 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9727\n",
      "Epoch 287/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0566 - pre_intent_loss: 0.0308 - pre_ner_loss: 0.0258 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9877 - val_loss: 0.4194 - val_pre_intent_loss: 0.2958 - val_pre_ner_loss: 0.1236 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 288/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0562 - pre_intent_loss: 0.0303 - pre_ner_loss: 0.0259 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9873 - val_loss: 0.4173 - val_pre_intent_loss: 0.2937 - val_pre_ner_loss: 0.1237 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 289/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0554 - pre_intent_loss: 0.0298 - pre_ner_loss: 0.0255 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9877 - val_loss: 0.4227 - val_pre_intent_loss: 0.2983 - val_pre_ner_loss: 0.1244 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 290/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0551 - pre_intent_loss: 0.0294 - pre_ner_loss: 0.0257 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9874 - val_loss: 0.4196 - val_pre_intent_loss: 0.2959 - val_pre_ner_loss: 0.1238 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 291/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0545 - pre_intent_loss: 0.0291 - pre_ner_loss: 0.0254 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9879 - val_loss: 0.4268 - val_pre_intent_loss: 0.3008 - val_pre_ner_loss: 0.1260 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 292/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0544 - pre_intent_loss: 0.0287 - pre_ner_loss: 0.0256 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9873 - val_loss: 0.4218 - val_pre_intent_loss: 0.2978 - val_pre_ner_loss: 0.1239 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9727\n",
      "Epoch 293/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0539 - pre_intent_loss: 0.0284 - pre_ner_loss: 0.0254 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9877 - val_loss: 0.4295 - val_pre_intent_loss: 0.3026 - val_pre_ner_loss: 0.1269 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 294/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0539 - pre_intent_loss: 0.0281 - pre_ner_loss: 0.0258 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9870 - val_loss: 0.4240 - val_pre_intent_loss: 0.2986 - val_pre_ner_loss: 0.1254 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 295/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0533 - pre_intent_loss: 0.0278 - pre_ner_loss: 0.0254 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9879 - val_loss: 0.4310 - val_pre_intent_loss: 0.3040 - val_pre_ner_loss: 0.1270 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 296/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0531 - pre_intent_loss: 0.0275 - pre_ner_loss: 0.0257 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9870 - val_loss: 0.4255 - val_pre_intent_loss: 0.2997 - val_pre_ner_loss: 0.1258 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 297/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0522 - pre_intent_loss: 0.0272 - pre_ner_loss: 0.0250 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9879 - val_loss: 0.4327 - val_pre_intent_loss: 0.3043 - val_pre_ner_loss: 0.1284 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 298/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0519 - pre_intent_loss: 0.0269 - pre_ner_loss: 0.0250 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9878 - val_loss: 0.4285 - val_pre_intent_loss: 0.3031 - val_pre_ner_loss: 0.1254 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 299/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0508 - pre_intent_loss: 0.0267 - pre_ner_loss: 0.0241 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9884 - val_loss: 0.4384 - val_pre_intent_loss: 0.3046 - val_pre_ner_loss: 0.1339 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 300/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0504 - pre_intent_loss: 0.0263 - pre_ner_loss: 0.0241 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9881 - val_loss: 0.4378 - val_pre_intent_loss: 0.3069 - val_pre_ner_loss: 0.1309 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 301/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0496 - pre_intent_loss: 0.0261 - pre_ner_loss: 0.0235 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9883 - val_loss: 0.4398 - val_pre_intent_loss: 0.3049 - val_pre_ner_loss: 0.1350 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 302/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0494 - pre_intent_loss: 0.0258 - pre_ner_loss: 0.0236 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9881 - val_loss: 0.4417 - val_pre_intent_loss: 0.3103 - val_pre_ner_loss: 0.1313 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 303/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0493 - pre_intent_loss: 0.0256 - pre_ner_loss: 0.0238 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9880 - val_loss: 0.4416 - val_pre_intent_loss: 0.3083 - val_pre_ner_loss: 0.1333 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 304/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0492 - pre_intent_loss: 0.0253 - pre_ner_loss: 0.0239 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9879 - val_loss: 0.4444 - val_pre_intent_loss: 0.3099 - val_pre_ner_loss: 0.1345 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 305/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0499 - pre_intent_loss: 0.0251 - pre_ner_loss: 0.0248 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9876 - val_loss: 0.4463 - val_pre_intent_loss: 0.3117 - val_pre_ner_loss: 0.1346 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 306/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0517 - pre_intent_loss: 0.0248 - pre_ner_loss: 0.0268 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9863 - val_loss: 0.4344 - val_pre_intent_loss: 0.3073 - val_pre_ner_loss: 0.1271 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 307/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0520 - pre_intent_loss: 0.0246 - pre_ner_loss: 0.0274 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9871 - val_loss: 0.4394 - val_pre_intent_loss: 0.3100 - val_pre_ner_loss: 0.1294 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 308/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0516 - pre_intent_loss: 0.0244 - pre_ner_loss: 0.0272 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9869 - val_loss: 0.4336 - val_pre_intent_loss: 0.3015 - val_pre_ner_loss: 0.1322 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 309/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0501 - pre_intent_loss: 0.0242 - pre_ner_loss: 0.0259 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9871 - val_loss: 0.4295 - val_pre_intent_loss: 0.3062 - val_pre_ner_loss: 0.1233 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 310/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0498 - pre_intent_loss: 0.0239 - pre_ner_loss: 0.0259 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9877 - val_loss: 0.4385 - val_pre_intent_loss: 0.3062 - val_pre_ner_loss: 0.1323 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 311/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0489 - pre_intent_loss: 0.0236 - pre_ner_loss: 0.0253 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9872 - val_loss: 0.4275 - val_pre_intent_loss: 0.2969 - val_pre_ner_loss: 0.1306 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 312/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0482 - pre_intent_loss: 0.0235 - pre_ner_loss: 0.0247 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9879 - val_loss: 0.4414 - val_pre_intent_loss: 0.3098 - val_pre_ner_loss: 0.1315 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 313/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0488 - pre_intent_loss: 0.0233 - pre_ner_loss: 0.0255 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9875 - val_loss: 0.4505 - val_pre_intent_loss: 0.3085 - val_pre_ner_loss: 0.1420 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 314/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0482 - pre_intent_loss: 0.0231 - pre_ner_loss: 0.0251 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9879 - val_loss: 0.4422 - val_pre_intent_loss: 0.3115 - val_pre_ner_loss: 0.1307 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 315/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0481 - pre_intent_loss: 0.0228 - pre_ner_loss: 0.0253 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9874 - val_loss: 0.4481 - val_pre_intent_loss: 0.3109 - val_pre_ner_loss: 0.1372 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 316/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0468 - pre_intent_loss: 0.0226 - pre_ner_loss: 0.0241 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9880 - val_loss: 0.4439 - val_pre_intent_loss: 0.3110 - val_pre_ner_loss: 0.1329 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 317/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0451 - pre_intent_loss: 0.0224 - pre_ner_loss: 0.0227 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9887 - val_loss: 0.4453 - val_pre_intent_loss: 0.3122 - val_pre_ner_loss: 0.1332 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 318/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0460 - pre_intent_loss: 0.0222 - pre_ner_loss: 0.0238 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9881 - val_loss: 0.4427 - val_pre_intent_loss: 0.2935 - val_pre_ner_loss: 0.1492 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9654\n",
      "Epoch 319/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0486 - pre_intent_loss: 0.0222 - pre_ner_loss: 0.0264 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9875 - val_loss: 0.4550 - val_pre_intent_loss: 0.3170 - val_pre_ner_loss: 0.1380 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 320/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0568 - pre_intent_loss: 0.0221 - pre_ner_loss: 0.0347 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9851 - val_loss: 0.4509 - val_pre_intent_loss: 0.3082 - val_pre_ner_loss: 0.1427 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 321/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0587 - pre_intent_loss: 0.0221 - pre_ner_loss: 0.0367 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9864 - val_loss: 0.4424 - val_pre_intent_loss: 0.3085 - val_pre_ner_loss: 0.1339 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 322/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0478 - pre_intent_loss: 0.0217 - pre_ner_loss: 0.0261 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9872 - val_loss: 0.4502 - val_pre_intent_loss: 0.3106 - val_pre_ner_loss: 0.1397 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 323/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0481 - pre_intent_loss: 0.0213 - pre_ner_loss: 0.0268 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9873 - val_loss: 0.4418 - val_pre_intent_loss: 0.2991 - val_pre_ner_loss: 0.1427 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 324/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0442 - pre_intent_loss: 0.0211 - pre_ner_loss: 0.0231 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9892 - val_loss: 0.4434 - val_pre_intent_loss: 0.3088 - val_pre_ner_loss: 0.1346 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9724\n",
      "Epoch 325/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0426 - pre_intent_loss: 0.0208 - pre_ner_loss: 0.0218 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9889 - val_loss: 0.4439 - val_pre_intent_loss: 0.3053 - val_pre_ner_loss: 0.1386 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 326/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0415 - pre_intent_loss: 0.0207 - pre_ner_loss: 0.0208 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9896 - val_loss: 0.4429 - val_pre_intent_loss: 0.3088 - val_pre_ner_loss: 0.1341 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 327/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0408 - pre_intent_loss: 0.0205 - pre_ner_loss: 0.0204 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9892 - val_loss: 0.4525 - val_pre_intent_loss: 0.3115 - val_pre_ner_loss: 0.1410 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 328/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0401 - pre_intent_loss: 0.0203 - pre_ner_loss: 0.0198 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9896 - val_loss: 0.4433 - val_pre_intent_loss: 0.3111 - val_pre_ner_loss: 0.1322 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 329/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0398 - pre_intent_loss: 0.0201 - pre_ner_loss: 0.0197 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9895 - val_loss: 0.4518 - val_pre_intent_loss: 0.3135 - val_pre_ner_loss: 0.1383 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 330/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0397 - pre_intent_loss: 0.0199 - pre_ner_loss: 0.0198 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9895 - val_loss: 0.4496 - val_pre_intent_loss: 0.3127 - val_pre_ner_loss: 0.1369 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 331/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0392 - pre_intent_loss: 0.0197 - pre_ner_loss: 0.0194 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9898 - val_loss: 0.4511 - val_pre_intent_loss: 0.3147 - val_pre_ner_loss: 0.1364 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 332/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0389 - pre_intent_loss: 0.0196 - pre_ner_loss: 0.0194 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9897 - val_loss: 0.4522 - val_pre_intent_loss: 0.3147 - val_pre_ner_loss: 0.1375 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9716\n",
      "Epoch 333/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0386 - pre_intent_loss: 0.0194 - pre_ner_loss: 0.0192 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9897 - val_loss: 0.4557 - val_pre_intent_loss: 0.3160 - val_pre_ner_loss: 0.1396 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 334/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0386 - pre_intent_loss: 0.0193 - pre_ner_loss: 0.0194 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9896 - val_loss: 0.4530 - val_pre_intent_loss: 0.3171 - val_pre_ner_loss: 0.1360 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 335/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0386 - pre_intent_loss: 0.0191 - pre_ner_loss: 0.0194 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9898 - val_loss: 0.4548 - val_pre_intent_loss: 0.3190 - val_pre_ner_loss: 0.1359 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 336/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0389 - pre_intent_loss: 0.0189 - pre_ner_loss: 0.0200 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9896 - val_loss: 0.4597 - val_pre_intent_loss: 0.3183 - val_pre_ner_loss: 0.1414 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 337/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0380 - pre_intent_loss: 0.0188 - pre_ner_loss: 0.0192 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9896 - val_loss: 0.4578 - val_pre_intent_loss: 0.3204 - val_pre_ner_loss: 0.1375 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 338/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0377 - pre_intent_loss: 0.0186 - pre_ner_loss: 0.0190 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9899 - val_loss: 0.4601 - val_pre_intent_loss: 0.3201 - val_pre_ner_loss: 0.1400 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 339/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0379 - pre_intent_loss: 0.0185 - pre_ner_loss: 0.0194 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9897 - val_loss: 0.4644 - val_pre_intent_loss: 0.3234 - val_pre_ner_loss: 0.1410 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 340/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0384 - pre_intent_loss: 0.0184 - pre_ner_loss: 0.0200 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9897 - val_loss: 0.4456 - val_pre_intent_loss: 0.3050 - val_pre_ner_loss: 0.1406 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 341/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0375 - pre_intent_loss: 0.0188 - pre_ner_loss: 0.0187 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9901 - val_loss: 0.4525 - val_pre_intent_loss: 0.3165 - val_pre_ner_loss: 0.1360 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 342/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0379 - pre_intent_loss: 0.0189 - pre_ner_loss: 0.0190 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9900 - val_loss: 0.4749 - val_pre_intent_loss: 0.3246 - val_pre_ner_loss: 0.1503 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 343/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0379 - pre_intent_loss: 0.0186 - pre_ner_loss: 0.0193 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9898 - val_loss: 0.4472 - val_pre_intent_loss: 0.3122 - val_pre_ner_loss: 0.1350 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 344/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0397 - pre_intent_loss: 0.0184 - pre_ner_loss: 0.0213 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9890 - val_loss: 0.4803 - val_pre_intent_loss: 0.3354 - val_pre_ner_loss: 0.1448 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 345/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0436 - pre_intent_loss: 0.0214 - pre_ner_loss: 0.0222 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9893 - val_loss: 0.4549 - val_pre_intent_loss: 0.3163 - val_pre_ner_loss: 0.1386 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 346/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0402 - pre_intent_loss: 0.0184 - pre_ner_loss: 0.0217 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9889 - val_loss: 0.4557 - val_pre_intent_loss: 0.3154 - val_pre_ner_loss: 0.1404 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 347/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0646 - pre_intent_loss: 0.0365 - pre_ner_loss: 0.0280 - pre_intent_accuracy: 0.9875 - pre_ner_accuracy: 0.9877 - val_loss: 0.6755 - val_pre_intent_loss: 0.5195 - val_pre_ner_loss: 0.1560 - val_pre_intent_accuracy: 0.8802 - val_pre_ner_accuracy: 0.9612\n",
      "Epoch 348/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.2682 - pre_intent_loss: 0.2259 - pre_ner_loss: 0.0423 - pre_intent_accuracy: 0.9487 - pre_ner_accuracy: 0.9825 - val_loss: 0.6315 - val_pre_intent_loss: 0.4730 - val_pre_ner_loss: 0.1584 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9667\n",
      "Epoch 349/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.2537 - pre_intent_loss: 0.2044 - pre_ner_loss: 0.0492 - pre_intent_accuracy: 0.9415 - pre_ner_accuracy: 0.9815 - val_loss: 0.6129 - val_pre_intent_loss: 0.4588 - val_pre_ner_loss: 0.1541 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 350/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.2389 - pre_intent_loss: 0.1919 - pre_ner_loss: 0.0470 - pre_intent_accuracy: 0.9460 - pre_ner_accuracy: 0.9832 - val_loss: 0.9222 - val_pre_intent_loss: 0.7528 - val_pre_ner_loss: 0.1694 - val_pre_intent_accuracy: 0.8490 - val_pre_ner_accuracy: 0.9651\n",
      "Epoch 351/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.3829 - pre_intent_loss: 0.3324 - pre_ner_loss: 0.0505 - pre_intent_accuracy: 0.9165 - pre_ner_accuracy: 0.9837 - val_loss: 0.5260 - val_pre_intent_loss: 0.3836 - val_pre_ner_loss: 0.1423 - val_pre_intent_accuracy: 0.8958 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 352/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.1730 - pre_intent_loss: 0.1294 - pre_ner_loss: 0.0436 - pre_intent_accuracy: 0.9549 - pre_ner_accuracy: 0.9836 - val_loss: 0.4727 - val_pre_intent_loss: 0.3356 - val_pre_ner_loss: 0.1371 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 353/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0802 - pre_intent_loss: 0.0528 - pre_ner_loss: 0.0275 - pre_intent_accuracy: 0.9875 - pre_ner_accuracy: 0.9871 - val_loss: 0.4217 - val_pre_intent_loss: 0.2875 - val_pre_ner_loss: 0.1343 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 354/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0614 - pre_intent_loss: 0.0379 - pre_ner_loss: 0.0235 - pre_intent_accuracy: 0.9906 - pre_ner_accuracy: 0.9889 - val_loss: 0.4279 - val_pre_intent_loss: 0.2977 - val_pre_ner_loss: 0.1302 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 355/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0558 - pre_intent_loss: 0.0337 - pre_ner_loss: 0.0222 - pre_intent_accuracy: 0.9902 - pre_ner_accuracy: 0.9886 - val_loss: 0.4259 - val_pre_intent_loss: 0.2919 - val_pre_ner_loss: 0.1339 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 356/500\n",
      "35/35 [==============================] - 0s 9ms/step - loss: 0.0513 - pre_intent_loss: 0.0304 - pre_ner_loss: 0.0209 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9894 - val_loss: 0.4257 - val_pre_intent_loss: 0.2936 - val_pre_ner_loss: 0.1320 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 357/500\n",
      "35/35 [==============================] - 0s 9ms/step - loss: 0.0481 - pre_intent_loss: 0.0278 - pre_ner_loss: 0.0203 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9896 - val_loss: 0.4298 - val_pre_intent_loss: 0.2965 - val_pre_ner_loss: 0.1333 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9716\n",
      "Epoch 358/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0460 - pre_intent_loss: 0.0261 - pre_ner_loss: 0.0199 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9897 - val_loss: 0.4294 - val_pre_intent_loss: 0.2952 - val_pre_ner_loss: 0.1342 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 359/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0439 - pre_intent_loss: 0.0243 - pre_ner_loss: 0.0196 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9898 - val_loss: 0.4259 - val_pre_intent_loss: 0.2924 - val_pre_ner_loss: 0.1336 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9711\n",
      "Epoch 360/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0419 - pre_intent_loss: 0.0228 - pre_ner_loss: 0.0191 - pre_intent_accuracy: 0.9911 - pre_ner_accuracy: 0.9899 - val_loss: 0.4267 - val_pre_intent_loss: 0.2905 - val_pre_ner_loss: 0.1362 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 361/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0409 - pre_intent_loss: 0.0221 - pre_ner_loss: 0.0189 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9900 - val_loss: 0.4285 - val_pre_intent_loss: 0.2947 - val_pre_ner_loss: 0.1337 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 362/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0399 - pre_intent_loss: 0.0212 - pre_ner_loss: 0.0187 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9900 - val_loss: 0.4322 - val_pre_intent_loss: 0.2954 - val_pre_ner_loss: 0.1368 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 363/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0391 - pre_intent_loss: 0.0207 - pre_ner_loss: 0.0185 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9901 - val_loss: 0.4288 - val_pre_intent_loss: 0.2969 - val_pre_ner_loss: 0.1319 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 364/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0385 - pre_intent_loss: 0.0202 - pre_ner_loss: 0.0183 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9901 - val_loss: 0.4338 - val_pre_intent_loss: 0.2990 - val_pre_ner_loss: 0.1348 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 365/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0378 - pre_intent_loss: 0.0197 - pre_ner_loss: 0.0182 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9901 - val_loss: 0.4326 - val_pre_intent_loss: 0.3000 - val_pre_ner_loss: 0.1325 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 366/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0372 - pre_intent_loss: 0.0192 - pre_ner_loss: 0.0180 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9901 - val_loss: 0.4389 - val_pre_intent_loss: 0.3036 - val_pre_ner_loss: 0.1352 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 367/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0367 - pre_intent_loss: 0.0188 - pre_ner_loss: 0.0179 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9902 - val_loss: 0.4397 - val_pre_intent_loss: 0.3066 - val_pre_ner_loss: 0.1331 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 368/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0363 - pre_intent_loss: 0.0185 - pre_ner_loss: 0.0178 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9901 - val_loss: 0.4430 - val_pre_intent_loss: 0.3073 - val_pre_ner_loss: 0.1357 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 369/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0358 - pre_intent_loss: 0.0182 - pre_ner_loss: 0.0177 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9902 - val_loss: 0.4415 - val_pre_intent_loss: 0.3077 - val_pre_ner_loss: 0.1338 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 370/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0355 - pre_intent_loss: 0.0179 - pre_ner_loss: 0.0176 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9902 - val_loss: 0.4446 - val_pre_intent_loss: 0.3083 - val_pre_ner_loss: 0.1362 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 371/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0350 - pre_intent_loss: 0.0176 - pre_ner_loss: 0.0174 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9902 - val_loss: 0.4432 - val_pre_intent_loss: 0.3087 - val_pre_ner_loss: 0.1345 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 372/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0347 - pre_intent_loss: 0.0173 - pre_ner_loss: 0.0174 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9903 - val_loss: 0.4458 - val_pre_intent_loss: 0.3091 - val_pre_ner_loss: 0.1367 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 373/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0343 - pre_intent_loss: 0.0171 - pre_ner_loss: 0.0172 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9903 - val_loss: 0.4446 - val_pre_intent_loss: 0.3094 - val_pre_ner_loss: 0.1352 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 374/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0340 - pre_intent_loss: 0.0169 - pre_ner_loss: 0.0171 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9906 - val_loss: 0.4472 - val_pre_intent_loss: 0.3099 - val_pre_ner_loss: 0.1373 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 375/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0336 - pre_intent_loss: 0.0167 - pre_ner_loss: 0.0169 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9903 - val_loss: 0.4463 - val_pre_intent_loss: 0.3104 - val_pre_ner_loss: 0.1359 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 376/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0334 - pre_intent_loss: 0.0166 - pre_ner_loss: 0.0168 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9906 - val_loss: 0.4489 - val_pre_intent_loss: 0.3107 - val_pre_ner_loss: 0.1382 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 377/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0331 - pre_intent_loss: 0.0164 - pre_ner_loss: 0.0168 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9903 - val_loss: 0.4463 - val_pre_intent_loss: 0.3104 - val_pre_ner_loss: 0.1359 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 378/500\n",
      "35/35 [==============================] - 0s 9ms/step - loss: 0.0329 - pre_intent_loss: 0.0162 - pre_ner_loss: 0.0167 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9905 - val_loss: 0.4494 - val_pre_intent_loss: 0.3099 - val_pre_ner_loss: 0.1395 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 379/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0327 - pre_intent_loss: 0.0161 - pre_ner_loss: 0.0165 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9903 - val_loss: 0.4457 - val_pre_intent_loss: 0.3099 - val_pre_ner_loss: 0.1359 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 380/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0326 - pre_intent_loss: 0.0160 - pre_ner_loss: 0.0166 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9906 - val_loss: 0.4503 - val_pre_intent_loss: 0.3095 - val_pre_ner_loss: 0.1408 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 381/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0323 - pre_intent_loss: 0.0159 - pre_ner_loss: 0.0164 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9904 - val_loss: 0.4459 - val_pre_intent_loss: 0.3097 - val_pre_ner_loss: 0.1362 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 382/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0323 - pre_intent_loss: 0.0158 - pre_ner_loss: 0.0165 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9906 - val_loss: 0.4510 - val_pre_intent_loss: 0.3095 - val_pre_ner_loss: 0.1415 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 383/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0319 - pre_intent_loss: 0.0157 - pre_ner_loss: 0.0163 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9905 - val_loss: 0.4481 - val_pre_intent_loss: 0.3100 - val_pre_ner_loss: 0.1381 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 384/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0320 - pre_intent_loss: 0.0155 - pre_ner_loss: 0.0164 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9905 - val_loss: 0.4511 - val_pre_intent_loss: 0.3099 - val_pre_ner_loss: 0.1412 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 385/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0316 - pre_intent_loss: 0.0154 - pre_ner_loss: 0.0162 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9905 - val_loss: 0.4504 - val_pre_intent_loss: 0.3105 - val_pre_ner_loss: 0.1399 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 386/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0315 - pre_intent_loss: 0.0153 - pre_ner_loss: 0.0162 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9905 - val_loss: 0.4514 - val_pre_intent_loss: 0.3101 - val_pre_ner_loss: 0.1413 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 387/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0311 - pre_intent_loss: 0.0152 - pre_ner_loss: 0.0159 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9906 - val_loss: 0.4531 - val_pre_intent_loss: 0.3110 - val_pre_ner_loss: 0.1422 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 388/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0311 - pre_intent_loss: 0.0151 - pre_ner_loss: 0.0160 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9907 - val_loss: 0.4525 - val_pre_intent_loss: 0.3104 - val_pre_ner_loss: 0.1421 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 389/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0308 - pre_intent_loss: 0.0151 - pre_ner_loss: 0.0157 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9907 - val_loss: 0.4554 - val_pre_intent_loss: 0.3118 - val_pre_ner_loss: 0.1436 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 390/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0309 - pre_intent_loss: 0.0150 - pre_ner_loss: 0.0159 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9906 - val_loss: 0.4536 - val_pre_intent_loss: 0.3109 - val_pre_ner_loss: 0.1427 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 391/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0308 - pre_intent_loss: 0.0149 - pre_ner_loss: 0.0159 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9907 - val_loss: 0.4576 - val_pre_intent_loss: 0.3128 - val_pre_ner_loss: 0.1448 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 392/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0307 - pre_intent_loss: 0.0148 - pre_ner_loss: 0.0159 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9906 - val_loss: 0.4550 - val_pre_intent_loss: 0.3119 - val_pre_ner_loss: 0.1431 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 393/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0304 - pre_intent_loss: 0.0147 - pre_ner_loss: 0.0157 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9908 - val_loss: 0.4592 - val_pre_intent_loss: 0.3136 - val_pre_ner_loss: 0.1457 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 394/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0303 - pre_intent_loss: 0.0146 - pre_ner_loss: 0.0157 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9907 - val_loss: 0.4557 - val_pre_intent_loss: 0.3131 - val_pre_ner_loss: 0.1425 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 395/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0302 - pre_intent_loss: 0.0146 - pre_ner_loss: 0.0156 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9908 - val_loss: 0.4619 - val_pre_intent_loss: 0.3146 - val_pre_ner_loss: 0.1472 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 396/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0302 - pre_intent_loss: 0.0145 - pre_ner_loss: 0.0157 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9906 - val_loss: 0.4557 - val_pre_intent_loss: 0.3136 - val_pre_ner_loss: 0.1420 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 397/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0304 - pre_intent_loss: 0.0144 - pre_ner_loss: 0.0160 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9906 - val_loss: 0.4630 - val_pre_intent_loss: 0.3166 - val_pre_ner_loss: 0.1464 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 398/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0305 - pre_intent_loss: 0.0143 - pre_ner_loss: 0.0161 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9905 - val_loss: 0.4580 - val_pre_intent_loss: 0.3145 - val_pre_ner_loss: 0.1435 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 399/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0304 - pre_intent_loss: 0.0143 - pre_ner_loss: 0.0162 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9904 - val_loss: 0.4625 - val_pre_intent_loss: 0.3176 - val_pre_ner_loss: 0.1450 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 400/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0305 - pre_intent_loss: 0.0142 - pre_ner_loss: 0.0163 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9906 - val_loss: 0.4588 - val_pre_intent_loss: 0.3160 - val_pre_ner_loss: 0.1429 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 401/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0305 - pre_intent_loss: 0.0141 - pre_ner_loss: 0.0164 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9906 - val_loss: 0.4620 - val_pre_intent_loss: 0.3188 - val_pre_ner_loss: 0.1432 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 402/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0303 - pre_intent_loss: 0.0141 - pre_ner_loss: 0.0162 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9905 - val_loss: 0.4628 - val_pre_intent_loss: 0.3167 - val_pre_ner_loss: 0.1461 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 403/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0300 - pre_intent_loss: 0.0140 - pre_ner_loss: 0.0160 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9905 - val_loss: 0.4678 - val_pre_intent_loss: 0.3194 - val_pre_ner_loss: 0.1484 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 404/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0296 - pre_intent_loss: 0.0139 - pre_ner_loss: 0.0156 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9908 - val_loss: 0.4657 - val_pre_intent_loss: 0.3178 - val_pre_ner_loss: 0.1479 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 405/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0288 - pre_intent_loss: 0.0139 - pre_ner_loss: 0.0149 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9911 - val_loss: 0.4706 - val_pre_intent_loss: 0.3208 - val_pre_ner_loss: 0.1498 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 406/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0287 - pre_intent_loss: 0.0138 - pre_ner_loss: 0.0149 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9911 - val_loss: 0.4682 - val_pre_intent_loss: 0.3184 - val_pre_ner_loss: 0.1498 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 407/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0285 - pre_intent_loss: 0.0137 - pre_ner_loss: 0.0147 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9910 - val_loss: 0.4751 - val_pre_intent_loss: 0.3237 - val_pre_ner_loss: 0.1514 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 408/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0287 - pre_intent_loss: 0.0137 - pre_ner_loss: 0.0150 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9908 - val_loss: 0.4677 - val_pre_intent_loss: 0.3189 - val_pre_ner_loss: 0.1489 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 409/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0290 - pre_intent_loss: 0.0137 - pre_ner_loss: 0.0153 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9908 - val_loss: 0.4795 - val_pre_intent_loss: 0.3265 - val_pre_ner_loss: 0.1530 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 410/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0289 - pre_intent_loss: 0.0136 - pre_ner_loss: 0.0153 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9909 - val_loss: 0.4721 - val_pre_intent_loss: 0.3221 - val_pre_ner_loss: 0.1500 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 411/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0283 - pre_intent_loss: 0.0135 - pre_ner_loss: 0.0147 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9912 - val_loss: 0.4777 - val_pre_intent_loss: 0.3263 - val_pre_ner_loss: 0.1514 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 412/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0280 - pre_intent_loss: 0.0134 - pre_ner_loss: 0.0146 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9913 - val_loss: 0.4791 - val_pre_intent_loss: 0.3267 - val_pre_ner_loss: 0.1524 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 413/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0272 - pre_intent_loss: 0.0134 - pre_ner_loss: 0.0139 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9913 - val_loss: 0.4761 - val_pre_intent_loss: 0.3273 - val_pre_ner_loss: 0.1489 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 414/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0270 - pre_intent_loss: 0.0133 - pre_ner_loss: 0.0137 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9914 - val_loss: 0.4821 - val_pre_intent_loss: 0.3295 - val_pre_ner_loss: 0.1526 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 415/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0281 - pre_intent_loss: 0.0133 - pre_ner_loss: 0.0148 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9909 - val_loss: 0.4846 - val_pre_intent_loss: 0.3267 - val_pre_ner_loss: 0.1579 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9669\n",
      "Epoch 416/500\n",
      "35/35 [==============================] - 0s 10ms/step - loss: 0.0339 - pre_intent_loss: 0.0163 - pre_ner_loss: 0.0177 - pre_intent_accuracy: 0.9911 - pre_ner_accuracy: 0.9900 - val_loss: 0.5183 - val_pre_intent_loss: 0.3618 - val_pre_ner_loss: 0.1564 - val_pre_intent_accuracy: 0.9115 - val_pre_ner_accuracy: 0.9664\n",
      "Epoch 417/500\n",
      "35/35 [==============================] - 0s 10ms/step - loss: 0.0630 - pre_intent_loss: 0.0383 - pre_ner_loss: 0.0247 - pre_intent_accuracy: 0.9871 - pre_ner_accuracy: 0.9887 - val_loss: 0.4606 - val_pre_intent_loss: 0.2925 - val_pre_ner_loss: 0.1681 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9638\n",
      "Epoch 418/500\n",
      "35/35 [==============================] - 0s 9ms/step - loss: 0.0522 - pre_intent_loss: 0.0218 - pre_ner_loss: 0.0304 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9860 - val_loss: 0.4612 - val_pre_intent_loss: 0.3022 - val_pre_ner_loss: 0.1590 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9680\n",
      "Epoch 419/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0553 - pre_intent_loss: 0.0174 - pre_ner_loss: 0.0379 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9848 - val_loss: 0.4754 - val_pre_intent_loss: 0.3074 - val_pre_ner_loss: 0.1680 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9643\n",
      "Epoch 420/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0418 - pre_intent_loss: 0.0164 - pre_ner_loss: 0.0255 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9876 - val_loss: 0.4647 - val_pre_intent_loss: 0.3123 - val_pre_ner_loss: 0.1524 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9669\n",
      "Epoch 421/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0375 - pre_intent_loss: 0.0157 - pre_ner_loss: 0.0218 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9887 - val_loss: 0.4771 - val_pre_intent_loss: 0.3250 - val_pre_ner_loss: 0.1521 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9680\n",
      "Epoch 422/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0540 - pre_intent_loss: 0.0341 - pre_ner_loss: 0.0199 - pre_intent_accuracy: 0.9862 - pre_ner_accuracy: 0.9895 - val_loss: 0.4676 - val_pre_intent_loss: 0.3133 - val_pre_ner_loss: 0.1543 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 423/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0453 - pre_intent_loss: 0.0264 - pre_ner_loss: 0.0189 - pre_intent_accuracy: 0.9893 - pre_ner_accuracy: 0.9901 - val_loss: 0.4185 - val_pre_intent_loss: 0.2709 - val_pre_ner_loss: 0.1476 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 424/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0337 - pre_intent_loss: 0.0175 - pre_ner_loss: 0.0163 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9904 - val_loss: 0.4653 - val_pre_intent_loss: 0.3134 - val_pre_ner_loss: 0.1518 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 425/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0304 - pre_intent_loss: 0.0160 - pre_ner_loss: 0.0144 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9913 - val_loss: 0.4711 - val_pre_intent_loss: 0.3165 - val_pre_ner_loss: 0.1546 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 426/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0287 - pre_intent_loss: 0.0147 - pre_ner_loss: 0.0141 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9911 - val_loss: 0.4672 - val_pre_intent_loss: 0.3164 - val_pre_ner_loss: 0.1508 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 427/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0281 - pre_intent_loss: 0.0142 - pre_ner_loss: 0.0139 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9913 - val_loss: 0.4710 - val_pre_intent_loss: 0.3164 - val_pre_ner_loss: 0.1546 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 428/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0273 - pre_intent_loss: 0.0139 - pre_ner_loss: 0.0134 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9915 - val_loss: 0.4656 - val_pre_intent_loss: 0.3142 - val_pre_ner_loss: 0.1515 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 429/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0269 - pre_intent_loss: 0.0136 - pre_ner_loss: 0.0133 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9915 - val_loss: 0.4692 - val_pre_intent_loss: 0.3135 - val_pre_ner_loss: 0.1557 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 430/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0266 - pre_intent_loss: 0.0134 - pre_ner_loss: 0.0132 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9917 - val_loss: 0.4647 - val_pre_intent_loss: 0.3131 - val_pre_ner_loss: 0.1516 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 431/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0264 - pre_intent_loss: 0.0133 - pre_ner_loss: 0.0131 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9915 - val_loss: 0.4688 - val_pre_intent_loss: 0.3120 - val_pre_ner_loss: 0.1568 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 432/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0262 - pre_intent_loss: 0.0132 - pre_ner_loss: 0.0131 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9918 - val_loss: 0.4656 - val_pre_intent_loss: 0.3137 - val_pre_ner_loss: 0.1519 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 433/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0261 - pre_intent_loss: 0.0131 - pre_ner_loss: 0.0130 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9916 - val_loss: 0.4698 - val_pre_intent_loss: 0.3124 - val_pre_ner_loss: 0.1574 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 434/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0259 - pre_intent_loss: 0.0130 - pre_ner_loss: 0.0129 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9917 - val_loss: 0.4672 - val_pre_intent_loss: 0.3147 - val_pre_ner_loss: 0.1525 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 435/500\n",
      "35/35 [==============================] - 0s 8ms/step - loss: 0.0258 - pre_intent_loss: 0.0129 - pre_ner_loss: 0.0129 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9917 - val_loss: 0.4707 - val_pre_intent_loss: 0.3131 - val_pre_ner_loss: 0.1576 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9674\n",
      "Epoch 436/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0256 - pre_intent_loss: 0.0128 - pre_ner_loss: 0.0128 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9917 - val_loss: 0.4695 - val_pre_intent_loss: 0.3161 - val_pre_ner_loss: 0.1533 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 437/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0255 - pre_intent_loss: 0.0127 - pre_ner_loss: 0.0128 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9916 - val_loss: 0.4718 - val_pre_intent_loss: 0.3141 - val_pre_ner_loss: 0.1577 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9677\n",
      "Epoch 438/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0254 - pre_intent_loss: 0.0127 - pre_ner_loss: 0.0127 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9917 - val_loss: 0.4722 - val_pre_intent_loss: 0.3178 - val_pre_ner_loss: 0.1544 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 439/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0253 - pre_intent_loss: 0.0126 - pre_ner_loss: 0.0127 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9916 - val_loss: 0.4721 - val_pre_intent_loss: 0.3151 - val_pre_ner_loss: 0.1570 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9680\n",
      "Epoch 440/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0251 - pre_intent_loss: 0.0125 - pre_ner_loss: 0.0126 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9918 - val_loss: 0.4753 - val_pre_intent_loss: 0.3198 - val_pre_ner_loss: 0.1555 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 441/500\n",
      "35/35 [==============================] - 0s 7ms/step - loss: 0.0250 - pre_intent_loss: 0.0125 - pre_ner_loss: 0.0125 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9916 - val_loss: 0.4735 - val_pre_intent_loss: 0.3163 - val_pre_ner_loss: 0.1572 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 442/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0249 - pre_intent_loss: 0.0124 - pre_ner_loss: 0.0125 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9919 - val_loss: 0.4783 - val_pre_intent_loss: 0.3220 - val_pre_ner_loss: 0.1563 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 443/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0248 - pre_intent_loss: 0.0124 - pre_ner_loss: 0.0124 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9917 - val_loss: 0.4749 - val_pre_intent_loss: 0.3173 - val_pre_ner_loss: 0.1577 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 444/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0248 - pre_intent_loss: 0.0123 - pre_ner_loss: 0.0125 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9919 - val_loss: 0.4822 - val_pre_intent_loss: 0.3235 - val_pre_ner_loss: 0.1586 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 445/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0250 - pre_intent_loss: 0.0123 - pre_ner_loss: 0.0128 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9915 - val_loss: 0.4769 - val_pre_intent_loss: 0.3180 - val_pre_ner_loss: 0.1589 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 446/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0259 - pre_intent_loss: 0.0122 - pre_ner_loss: 0.0137 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9917 - val_loss: 0.4857 - val_pre_intent_loss: 0.3248 - val_pre_ner_loss: 0.1609 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 447/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0246 - pre_intent_loss: 0.0121 - pre_ner_loss: 0.0125 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9916 - val_loss: 0.4767 - val_pre_intent_loss: 0.3188 - val_pre_ner_loss: 0.1579 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 448/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0245 - pre_intent_loss: 0.0121 - pre_ner_loss: 0.0125 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9918 - val_loss: 0.4872 - val_pre_intent_loss: 0.3249 - val_pre_ner_loss: 0.1623 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 449/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0244 - pre_intent_loss: 0.0120 - pre_ner_loss: 0.0124 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9918 - val_loss: 0.4796 - val_pre_intent_loss: 0.3201 - val_pre_ner_loss: 0.1595 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 450/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0242 - pre_intent_loss: 0.0120 - pre_ner_loss: 0.0122 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9919 - val_loss: 0.4869 - val_pre_intent_loss: 0.3254 - val_pre_ner_loss: 0.1615 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 451/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0242 - pre_intent_loss: 0.0119 - pre_ner_loss: 0.0123 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9918 - val_loss: 0.4821 - val_pre_intent_loss: 0.3213 - val_pre_ner_loss: 0.1608 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 452/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0239 - pre_intent_loss: 0.0119 - pre_ner_loss: 0.0120 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9920 - val_loss: 0.4863 - val_pre_intent_loss: 0.3252 - val_pre_ner_loss: 0.1611 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 453/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0240 - pre_intent_loss: 0.0118 - pre_ner_loss: 0.0122 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9918 - val_loss: 0.4835 - val_pre_intent_loss: 0.3225 - val_pre_ner_loss: 0.1610 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 454/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0237 - pre_intent_loss: 0.0118 - pre_ner_loss: 0.0119 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9919 - val_loss: 0.4879 - val_pre_intent_loss: 0.3252 - val_pre_ner_loss: 0.1627 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9698\n",
      "Epoch 455/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0238 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0121 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9918 - val_loss: 0.4884 - val_pre_intent_loss: 0.3239 - val_pre_ner_loss: 0.1645 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9677\n",
      "Epoch 456/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0243 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0126 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9916 - val_loss: 0.4969 - val_pre_intent_loss: 0.3331 - val_pre_ner_loss: 0.1638 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9706\n",
      "Epoch 457/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0279 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0162 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9905 - val_loss: 0.5072 - val_pre_intent_loss: 0.3013 - val_pre_ner_loss: 0.2058 - val_pre_intent_accuracy: 0.9323 - val_pre_ner_accuracy: 0.9609\n",
      "Epoch 458/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0470 - pre_intent_loss: 0.0121 - pre_ner_loss: 0.0348 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9860 - val_loss: 0.5189 - val_pre_intent_loss: 0.3447 - val_pre_ner_loss: 0.1742 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9654\n",
      "Epoch 459/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0562 - pre_intent_loss: 0.0142 - pre_ner_loss: 0.0419 - pre_intent_accuracy: 0.9906 - pre_ner_accuracy: 0.9844 - val_loss: 0.5170 - val_pre_intent_loss: 0.3364 - val_pre_ner_loss: 0.1806 - val_pre_intent_accuracy: 0.9219 - val_pre_ner_accuracy: 0.9648\n",
      "Epoch 460/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0485 - pre_intent_loss: 0.0212 - pre_ner_loss: 0.0273 - pre_intent_accuracy: 0.9884 - pre_ner_accuracy: 0.9873 - val_loss: 0.5957 - val_pre_intent_loss: 0.4144 - val_pre_ner_loss: 0.1813 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9651\n",
      "Epoch 461/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.1404 - pre_intent_loss: 0.1074 - pre_ner_loss: 0.0329 - pre_intent_accuracy: 0.9696 - pre_ner_accuracy: 0.9877 - val_loss: 0.6841 - val_pre_intent_loss: 0.5078 - val_pre_ner_loss: 0.1763 - val_pre_intent_accuracy: 0.8906 - val_pre_ner_accuracy: 0.9638\n",
      "Epoch 462/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.1631 - pre_intent_loss: 0.1338 - pre_ner_loss: 0.0293 - pre_intent_accuracy: 0.9621 - pre_ner_accuracy: 0.9875 - val_loss: 0.8540 - val_pre_intent_loss: 0.6358 - val_pre_ner_loss: 0.2182 - val_pre_intent_accuracy: 0.8177 - val_pre_ner_accuracy: 0.9622\n",
      "Epoch 463/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.2479 - pre_intent_loss: 0.1833 - pre_ner_loss: 0.0646 - pre_intent_accuracy: 0.9241 - pre_ner_accuracy: 0.9823 - val_loss: 0.5384 - val_pre_intent_loss: 0.3715 - val_pre_ner_loss: 0.1669 - val_pre_intent_accuracy: 0.9167 - val_pre_ner_accuracy: 0.9654\n",
      "Epoch 464/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.1227 - pre_intent_loss: 0.0912 - pre_ner_loss: 0.0315 - pre_intent_accuracy: 0.9719 - pre_ner_accuracy: 0.9864 - val_loss: 0.5068 - val_pre_intent_loss: 0.3357 - val_pre_ner_loss: 0.1711 - val_pre_intent_accuracy: 0.9271 - val_pre_ner_accuracy: 0.9664\n",
      "Epoch 465/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0602 - pre_intent_loss: 0.0372 - pre_ner_loss: 0.0230 - pre_intent_accuracy: 0.9888 - pre_ner_accuracy: 0.9884 - val_loss: 0.4331 - val_pre_intent_loss: 0.2759 - val_pre_ner_loss: 0.1571 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 466/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0505 - pre_intent_loss: 0.0305 - pre_ner_loss: 0.0201 - pre_intent_accuracy: 0.9893 - pre_ner_accuracy: 0.9893 - val_loss: 0.4347 - val_pre_intent_loss: 0.2702 - val_pre_ner_loss: 0.1645 - val_pre_intent_accuracy: 0.9479 - val_pre_ner_accuracy: 0.9669\n",
      "Epoch 467/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0412 - pre_intent_loss: 0.0231 - pre_ner_loss: 0.0181 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9898 - val_loss: 0.4575 - val_pre_intent_loss: 0.2892 - val_pre_ner_loss: 0.1683 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 468/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0342 - pre_intent_loss: 0.0186 - pre_ner_loss: 0.0156 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9906 - val_loss: 0.4609 - val_pre_intent_loss: 0.2967 - val_pre_ner_loss: 0.1641 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 469/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0311 - pre_intent_loss: 0.0168 - pre_ner_loss: 0.0143 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9911 - val_loss: 0.4665 - val_pre_intent_loss: 0.3015 - val_pre_ner_loss: 0.1650 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 470/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0302 - pre_intent_loss: 0.0164 - pre_ner_loss: 0.0138 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9912 - val_loss: 0.4726 - val_pre_intent_loss: 0.3086 - val_pre_ner_loss: 0.1639 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 471/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0291 - pre_intent_loss: 0.0156 - pre_ner_loss: 0.0135 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9914 - val_loss: 0.4718 - val_pre_intent_loss: 0.3070 - val_pre_ner_loss: 0.1648 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 472/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0282 - pre_intent_loss: 0.0151 - pre_ner_loss: 0.0132 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9915 - val_loss: 0.4698 - val_pre_intent_loss: 0.3052 - val_pre_ner_loss: 0.1645 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 473/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0276 - pre_intent_loss: 0.0146 - pre_ner_loss: 0.0130 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9916 - val_loss: 0.4675 - val_pre_intent_loss: 0.3026 - val_pre_ner_loss: 0.1649 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 474/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0269 - pre_intent_loss: 0.0141 - pre_ner_loss: 0.0128 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9917 - val_loss: 0.4662 - val_pre_intent_loss: 0.3013 - val_pre_ner_loss: 0.1649 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 475/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0265 - pre_intent_loss: 0.0138 - pre_ner_loss: 0.0127 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9918 - val_loss: 0.4675 - val_pre_intent_loss: 0.3021 - val_pre_ner_loss: 0.1654 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 476/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0262 - pre_intent_loss: 0.0136 - pre_ner_loss: 0.0126 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9918 - val_loss: 0.4667 - val_pre_intent_loss: 0.3014 - val_pre_ner_loss: 0.1653 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 477/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0259 - pre_intent_loss: 0.0134 - pre_ner_loss: 0.0125 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9919 - val_loss: 0.4687 - val_pre_intent_loss: 0.3027 - val_pre_ner_loss: 0.1660 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 478/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0256 - pre_intent_loss: 0.0133 - pre_ner_loss: 0.0123 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9919 - val_loss: 0.4676 - val_pre_intent_loss: 0.3021 - val_pre_ner_loss: 0.1655 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 479/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0253 - pre_intent_loss: 0.0131 - pre_ner_loss: 0.0122 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9919 - val_loss: 0.4703 - val_pre_intent_loss: 0.3038 - val_pre_ner_loss: 0.1665 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 480/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0250 - pre_intent_loss: 0.0130 - pre_ner_loss: 0.0120 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9921 - val_loss: 0.4688 - val_pre_intent_loss: 0.3032 - val_pre_ner_loss: 0.1656 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9680\n",
      "Epoch 481/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0247 - pre_intent_loss: 0.0129 - pre_ner_loss: 0.0119 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9921 - val_loss: 0.4717 - val_pre_intent_loss: 0.3049 - val_pre_ner_loss: 0.1668 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 482/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0245 - pre_intent_loss: 0.0127 - pre_ner_loss: 0.0118 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9922 - val_loss: 0.4694 - val_pre_intent_loss: 0.3037 - val_pre_ner_loss: 0.1657 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9682\n",
      "Epoch 483/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0243 - pre_intent_loss: 0.0126 - pre_ner_loss: 0.0117 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9922 - val_loss: 0.4724 - val_pre_intent_loss: 0.3052 - val_pre_ner_loss: 0.1672 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 484/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0241 - pre_intent_loss: 0.0125 - pre_ner_loss: 0.0116 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9922 - val_loss: 0.4706 - val_pre_intent_loss: 0.3046 - val_pre_ner_loss: 0.1660 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9685\n",
      "Epoch 485/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0239 - pre_intent_loss: 0.0124 - pre_ner_loss: 0.0115 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9922 - val_loss: 0.4738 - val_pre_intent_loss: 0.3062 - val_pre_ner_loss: 0.1676 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 486/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0238 - pre_intent_loss: 0.0123 - pre_ner_loss: 0.0114 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9922 - val_loss: 0.4719 - val_pre_intent_loss: 0.3055 - val_pre_ner_loss: 0.1664 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 487/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0236 - pre_intent_loss: 0.0123 - pre_ner_loss: 0.0114 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9923 - val_loss: 0.4744 - val_pre_intent_loss: 0.3063 - val_pre_ner_loss: 0.1681 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 488/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0235 - pre_intent_loss: 0.0122 - pre_ner_loss: 0.0113 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9922 - val_loss: 0.4724 - val_pre_intent_loss: 0.3056 - val_pre_ner_loss: 0.1668 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 489/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0234 - pre_intent_loss: 0.0121 - pre_ner_loss: 0.0113 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9923 - val_loss: 0.4755 - val_pre_intent_loss: 0.3068 - val_pre_ner_loss: 0.1687 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 490/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0233 - pre_intent_loss: 0.0120 - pre_ner_loss: 0.0112 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9923 - val_loss: 0.4734 - val_pre_intent_loss: 0.3062 - val_pre_ner_loss: 0.1672 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 491/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0231 - pre_intent_loss: 0.0120 - pre_ner_loss: 0.0112 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9923 - val_loss: 0.4767 - val_pre_intent_loss: 0.3075 - val_pre_ner_loss: 0.1693 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 492/500\n",
      "35/35 [==============================] - 0s 5ms/step - loss: 0.0231 - pre_intent_loss: 0.0119 - pre_ner_loss: 0.0111 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9924 - val_loss: 0.4744 - val_pre_intent_loss: 0.3068 - val_pre_ner_loss: 0.1676 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 493/500\n",
      "35/35 [==============================] - 0s 6ms/step - loss: 0.0229 - pre_intent_loss: 0.0119 - pre_ner_loss: 0.0111 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9923 - val_loss: 0.4778 - val_pre_intent_loss: 0.3080 - val_pre_ner_loss: 0.1698 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 494/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0229 - pre_intent_loss: 0.0118 - pre_ner_loss: 0.0110 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9924 - val_loss: 0.4751 - val_pre_intent_loss: 0.3071 - val_pre_ner_loss: 0.1680 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 495/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0227 - pre_intent_loss: 0.0118 - pre_ner_loss: 0.0110 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9924 - val_loss: 0.4786 - val_pre_intent_loss: 0.3084 - val_pre_ner_loss: 0.1703 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9695\n",
      "Epoch 496/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0227 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0109 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9925 - val_loss: 0.4757 - val_pre_intent_loss: 0.3074 - val_pre_ner_loss: 0.1684 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 497/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0225 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0109 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9924 - val_loss: 0.4794 - val_pre_intent_loss: 0.3087 - val_pre_ner_loss: 0.1707 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 498/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0225 - pre_intent_loss: 0.0116 - pre_ner_loss: 0.0108 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9925 - val_loss: 0.4762 - val_pre_intent_loss: 0.3075 - val_pre_ner_loss: 0.1687 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 499/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0224 - pre_intent_loss: 0.0116 - pre_ner_loss: 0.0108 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9925 - val_loss: 0.4801 - val_pre_intent_loss: 0.3089 - val_pre_ner_loss: 0.1713 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9693\n",
      "Epoch 500/500\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0223 - pre_intent_loss: 0.0115 - pre_ner_loss: 0.0108 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9926 - val_loss: 0.4766 - val_pre_intent_loss: 0.3074 - val_pre_ner_loss: 0.1692 - val_pre_intent_accuracy: 0.9427 - val_pre_ner_accuracy: 0.9685\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f86d474b1d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_dataset,epochs=params['epochs'],validation_data=valid_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('./model_weight/model_6.17.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Epoch 498/500\n",
    "36/36 [==============================] - 0s 7ms/step - loss: 0.0321 - pre_intent_loss: 0.0149 - pre_ner_loss: 0.0172 - pre_intent_accuracy: 0.9917 - pre_ner_accuracy: 0.9939 - val_loss: 0.4498 - val_pre_intent_loss: 0.3283 - val_pre_ner_loss: 0.1215 - val_pre_intent_accuracy: 0.9412 - val_pre_ner_accuracy: 0.9763\n",
    "Epoch 499/500\n",
    "36/36 [==============================] - 0s 6ms/step - loss: 0.0322 - pre_intent_loss: 0.0148 - pre_ner_loss: 0.0174 - pre_intent_accuracy: 0.9917 - pre_ner_accuracy: 0.9940 - val_loss: 0.4503 - val_pre_intent_loss: 0.3310 - val_pre_ner_loss: 0.1193 - val_pre_intent_accuracy: 0.9412 - val_pre_ner_accuracy: 0.9767\n",
    "Epoch 500/500\n",
    "36/36 [==============================] - 0s 6ms/step - loss: 0.0317 - pre_intent_loss: 0.0147 - pre_ner_loss: 0.0169 - pre_intent_accuracy: 0.9917 - pre_ner_accuracy: 0.9941 - val_loss: 0.4513 - val_pre_intent_loss: 0.3295 - val_pre_ner_loss: 0.1218 - val_pre_intent_accuracy: 0.9412 - val_pre_ner_accuracy: 0.9757\n",
    "<tensorflow.python.keras.callbacks.History at 0x7f0395619e50>"
   ]
  }
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